Overview

Dataset statistics

Number of variables101
Number of observations42757
Missing cells1419102
Missing cells (%)32.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 MiB
Average record size in memory808.0 B

Variable types

Categorical63
Numeric18
Unsupported20

Warnings

ExamCenterID has constant value "1.0" Constant
PCLLocationID has constant value "0.0" Constant
InstitutionTypeID has constant value "0.0" Constant
RejectReason has constant value "Rejected Duplicate Entry" Constant
ExamDurationMinute has constant value "120.0" Constant
IsResultImmediately has constant value "0.0" Constant
PageSize has constant value "20.0" Constant
FacultyID has constant value "2.0" Constant
LevelID has constant value "1.0" Constant
FacultyName has constant value "Engineering" Constant
LevelName has constant value "BE / B.Arch." Constant
FormNo has a high cardinality: 42755 distinct values High cardinality
FirstName has a high cardinality: 10332 distinct values High cardinality
MiddleName has a high cardinality: 1014 distinct values High cardinality
LastName has a high cardinality: 3287 distinct values High cardinality
MunicipalityVdc has a high cardinality: 10899 distinct values High cardinality
SLCSchoolName has a high cardinality: 26224 distinct values High cardinality
PCLSchoolName has a high cardinality: 15347 distinct values High cardinality
FatherFirstName has a high cardinality: 9331 distinct values High cardinality
FatherMiddleName has a high cardinality: 1477 distinct values High cardinality
FatherLastName has a high cardinality: 3633 distinct values High cardinality
MotherFirstName has a high cardinality: 8196 distinct values High cardinality
MotherMiddleName has a high cardinality: 993 distinct values High cardinality
MotherLastName has a high cardinality: 3801 distinct values High cardinality
DistrictName has a high cardinality: 75 distinct values High cardinality
FullName has a high cardinality: 36272 distinct values High cardinality
StudentCode has a high cardinality: 31342 distinct values High cardinality
Email has a high cardinality: 30138 distinct values High cardinality
SLCBoardSpecify has a high cardinality: 113 distinct values High cardinality
PCLBoardSpecify has a high cardinality: 117 distinct values High cardinality
SLCSchoolFullAddress has a high cardinality: 504 distinct values High cardinality
PCLSchoolFullAddress has a high cardinality: 619 distinct values High cardinality
Password has a high cardinality: 31497 distinct values High cardinality
ExamSessionName has a high cardinality: 155 distinct values High cardinality
SLCPassedYear is highly correlated with PCLPassedYear and 1 other fieldsHigh correlation
SLCPercentage is highly correlated with PCLPercentage and 5 other fieldsHigh correlation
PCLPercentage is highly correlated with SLCPercentage and 5 other fieldsHigh correlation
PCLPassedYear is highly correlated with SLCPassedYear and 1 other fieldsHigh correlation
EthnicGroupID is highly correlated with SLCPassedYear and 2 other fieldsHigh correlation
DistrictCode is highly correlated with ZoneIDHigh correlation
EntranceScore is highly correlated with EntranceRankHigh correlation
EntranceRank is highly correlated with EntranceScoreHigh correlation
StudentID is highly correlated with Capacity and 2 other fieldsHigh correlation
ExamSessionID is highly correlated with FormIndexHigh correlation
ZoneID is highly correlated with DistrictCode and 1 other fieldsHigh correlation
DistrictID is highly correlated with ZoneID and 1 other fieldsHigh correlation
PCLEquivalentID is highly correlated with PCLBoardIDHigh correlation
PCLBoardID is highly correlated with PCLEquivalentIDHigh correlation
SLCSchoolDistrictID is highly correlated with DistrictID and 1 other fieldsHigh correlation
PCLSchoolDistrictID is highly correlated with SLCSchoolDistrictIDHigh correlation
IsAccepted is highly correlated with FormStatusHigh correlation
Capacity is highly correlated with SLCPercentage and 5 other fieldsHigh correlation
FormStatus is highly correlated with IsAcceptedHigh correlation
HasStudentAttemptedExam is highly correlated with SLCPercentage and 5 other fieldsHigh correlation
FormIndex is highly correlated with ExamSessionIDHigh correlation
FiscalYearID is highly correlated with SLCPercentage and 5 other fieldsHigh correlation
FiscalYearName is highly correlated with SLCPercentage and 6 other fieldsHigh correlation
PhotoDocumentID is highly correlated with SLCPercentage and 6 other fieldsHigh correlation
SLCPassedYear is highly correlated with SLCPercentage and 7 other fieldsHigh correlation
SLCPercentage is highly correlated with SLCPassedYear and 6 other fieldsHigh correlation
PCLPercentage is highly correlated with SLCPercentage and 5 other fieldsHigh correlation
PCLPassedYear is highly correlated with SLCPassedYear and 2 other fieldsHigh correlation
EthnicGroupID is highly correlated with SLCPassedYear and 2 other fieldsHigh correlation
DistrictCode is highly correlated with ZoneIDHigh correlation
EntranceScore is highly correlated with EntranceRankHigh correlation
EntranceRank is highly correlated with EntranceScoreHigh correlation
StudentID is highly correlated with Capacity and 1 other fieldsHigh correlation
NationalityID is highly correlated with CountryIDHigh correlation
CountryID is highly correlated with NationalityIDHigh correlation
ExamSessionID is highly correlated with FormIndexHigh correlation
ZoneID is highly correlated with DistrictCode and 1 other fieldsHigh correlation
DistrictID is highly correlated with ZoneID and 1 other fieldsHigh correlation
PCLEquivalentID is highly correlated with PCLBoardIDHigh correlation
PCLBoardID is highly correlated with PCLEquivalentIDHigh correlation
SLCSchoolDistrictID is highly correlated with DistrictID and 1 other fieldsHigh correlation
PCLSchoolDistrictID is highly correlated with SLCSchoolDistrictIDHigh correlation
IsAccepted is highly correlated with FormStatusHigh correlation
Capacity is highly correlated with SLCPassedYear and 6 other fieldsHigh correlation
FormStatus is highly correlated with IsAcceptedHigh correlation
HasStudentAttemptedExam is highly correlated with SLCPassedYear and 6 other fieldsHigh correlation
FormIndex is highly correlated with ExamSessionIDHigh correlation
FiscalYearID is highly correlated with SLCPassedYear and 6 other fieldsHigh correlation
FiscalYearName is highly correlated with SLCPassedYear and 8 other fieldsHigh correlation
PhotoDocumentID is highly correlated with SLCPassedYear and 6 other fieldsHigh correlation
SLCPassedYear is highly correlated with PCLPassedYear and 6 other fieldsHigh correlation
SLCPercentage is highly correlated with PCLPercentage and 4 other fieldsHigh correlation
PCLPercentage is highly correlated with SLCPercentage and 1 other fieldsHigh correlation
PCLPassedYear is highly correlated with SLCPassedYear and 1 other fieldsHigh correlation
EthnicGroupID is highly correlated with SLCPassedYear and 2 other fieldsHigh correlation
EntranceScore is highly correlated with EntranceRankHigh correlation
EntranceRank is highly correlated with EntranceScoreHigh correlation
StudentID is highly correlated with CapacityHigh correlation
NationalityID is highly correlated with CountryIDHigh correlation
CountryID is highly correlated with NationalityIDHigh correlation
DistrictID is highly correlated with SLCSchoolDistrictIDHigh correlation
PCLEquivalentID is highly correlated with PCLBoardIDHigh correlation
PCLBoardID is highly correlated with PCLEquivalentIDHigh correlation
SLCSchoolDistrictID is highly correlated with DistrictID and 1 other fieldsHigh correlation
PCLSchoolDistrictID is highly correlated with SLCSchoolDistrictIDHigh correlation
IsAccepted is highly correlated with FormStatusHigh correlation
Capacity is highly correlated with SLCPassedYear and 5 other fieldsHigh correlation
FormStatus is highly correlated with IsAcceptedHigh correlation
HasStudentAttemptedExam is highly correlated with SLCPassedYear and 5 other fieldsHigh correlation
FiscalYearID is highly correlated with SLCPassedYear and 5 other fieldsHigh correlation
FiscalYearName is highly correlated with SLCPassedYear and 5 other fieldsHigh correlation
PhotoDocumentID is highly correlated with SLCPassedYear and 5 other fieldsHigh correlation
ExamSessionID is highly correlated with FiscalYearID and 7 other fieldsHigh correlation
SLCBoardID is highly correlated with SLCBoardNameHigh correlation
IsAccepted is highly correlated with FormStatusHigh correlation
FiscalYearID is highly correlated with ExamSessionID and 9 other fieldsHigh correlation
Active is highly correlated with FormStatusHigh correlation
CreatedBy is highly correlated with ModifiedByHigh correlation
CountryID is highly correlated with NationalityName and 1 other fieldsHigh correlation
SLCBoardName is highly correlated with SLCBoardIDHigh correlation
DistrictID is highly correlated with SLCSchoolDistrictID and 4 other fieldsHigh correlation
Capacity is highly correlated with ExamSessionID and 8 other fieldsHigh correlation
IdentificationTypeName is highly correlated with IdentificationTypeIDHigh correlation
SLCPercentage is highly correlated with FiscalYearID and 8 other fieldsHigh correlation
NationalityName is highly correlated with CountryID and 1 other fieldsHigh correlation
PhotoDocumentID is highly correlated with ExamSessionID and 10 other fieldsHigh correlation
PCLPassedYearCalendar is highly correlated with SLCPassedYearCalendar and 4 other fieldsHigh correlation
EthnicGroupID is highly correlated with SLCPercentage and 4 other fieldsHigh correlation
ModifiedBy is highly correlated with FiscalYearID and 5 other fieldsHigh correlation
FormStatus is highly correlated with IsAccepted and 1 other fieldsHigh correlation
IdentificationTypeID is highly correlated with IdentificationTypeNameHigh correlation
SLCPassedYearCalendar is highly correlated with PCLPassedYearCalendar and 2 other fieldsHigh correlation
PCLBoardID is highly correlated with PCLPassedYearCalendar and 2 other fieldsHigh correlation
SLCPassedYear is highly correlated with SLCPassedYearCalendar and 2 other fieldsHigh correlation
PCLBoardName is highly correlated with PCLPassedYearCalendar and 4 other fieldsHigh correlation
PCLEquivalentID is highly correlated with PCLBoardID and 1 other fieldsHigh correlation
PCLPassedYear is highly correlated with PCLPassedYearCalendar and 2 other fieldsHigh correlation
HasStudentAttemptedExam is highly correlated with ExamSessionID and 9 other fieldsHigh correlation
SLCSchoolDistrictID is highly correlated with DistrictID and 4 other fieldsHigh correlation
PCLSchoolDistrictID is highly correlated with DistrictID and 4 other fieldsHigh correlation
PCLResultTypeID is highly correlated with PCLPassedYearCalendarHigh correlation
EntranceRank is highly correlated with EthnicGroupID and 1 other fieldsHigh correlation
ShiftName is highly correlated with ShiftIDHigh correlation
FormIndex is highly correlated with ExamSessionID and 2 other fieldsHigh correlation
NationalityID is highly correlated with CountryID and 1 other fieldsHigh correlation
PCLPercentage is highly correlated with FiscalYearID and 8 other fieldsHigh correlation
DistrictName is highly correlated with DistrictID and 5 other fieldsHigh correlation
EntranceScore is highly correlated with EthnicGroupID and 2 other fieldsHigh correlation
date is highly correlated with ExamSessionID and 9 other fieldsHigh correlation
FiscalYearName is highly correlated with ExamSessionID and 8 other fieldsHigh correlation
DistrictCode is highly correlated with DistrictID and 4 other fieldsHigh correlation
ShiftID is highly correlated with ShiftNameHigh correlation
ZoneID is highly correlated with DistrictID and 4 other fieldsHigh correlation
StudentID is highly correlated with ExamSessionID and 10 other fieldsHigh correlation
MiddleName has 33685 (78.8%) missing values Missing
PCLPercentage has 33398 (78.1%) missing values Missing
PCLPassedYear has 33287 (77.9%) missing values Missing
EthnicGroupID has 42750 (> 99.9%) missing values Missing
FatherMiddleName has 13539 (31.7%) missing values Missing
MotherMiddleName has 26118 (61.1%) missing values Missing
EntranceScore has 22859 (53.5%) missing values Missing
EntranceRank has 22859 (53.5%) missing values Missing
StudentID has 10741 (25.1%) missing values Missing
VoucherNo has 10741 (25.1%) missing values Missing
StudentCode has 10741 (25.1%) missing values Missing
ContactNo has 10741 (25.1%) missing values Missing
Email has 10741 (25.1%) missing values Missing
NationalityID has 10741 (25.1%) missing values Missing
CountryID has 10741 (25.1%) missing values Missing
IndianEmbassyID has 42757 (100.0%) missing values Missing
ExamCenterID has 10745 (25.1%) missing values Missing
ExamSessionID has 10745 (25.1%) missing values Missing
ZoneID has 10769 (25.2%) missing values Missing
DistrictID has 10769 (25.2%) missing values Missing
SLCEquivalentID has 10741 (25.1%) missing values Missing
SLCBoardID has 10741 (25.1%) missing values Missing
PCLEquivalentID has 10741 (25.1%) missing values Missing
PCLBoardID has 10741 (25.1%) missing values Missing
PCLResultTypeID has 10741 (25.1%) missing values Missing
PCLLocationID has 10741 (25.1%) missing values Missing
EthnicGroupSpecify has 42755 (> 99.9%) missing values Missing
SLCEquivalentSpecify has 41761 (97.7%) missing values Missing
SLCBoardSpecify has 42266 (98.9%) missing values Missing
PCLEquivalentSpecify has 42674 (99.8%) missing values Missing
PCLBoardSpecify has 42027 (98.3%) missing values Missing
SLCSchoolDistrictID has 11138 (26.0%) missing values Missing
SLCSchoolFullAddress has 42215 (98.7%) missing values Missing
PCLSchoolDistrictID has 11453 (26.8%) missing values Missing
PCLSchoolFullAddress has 41896 (98.0%) missing values Missing
InstitutionTypeID has 10741 (25.1%) missing values Missing
IdentificationTypeID has 10741 (25.1%) missing values Missing
IdentificationNo has 10741 (25.1%) missing values Missing
FormSubmittedDate has 10743 (25.1%) missing values Missing
CheckedBy has 42757 (100.0%) missing values Missing
IsAccepted has 10770 (25.2%) missing values Missing
IsSubmitted has 10741 (25.1%) missing values Missing
Password has 10741 (25.1%) missing values Missing
RejectReason has 42752 (> 99.9%) missing values Missing
ExamSessionName has 10745 (25.1%) missing values Missing
ExamSessionDateAD has 10745 (25.1%) missing values Missing
ExamSessionDateBS has 10745 (25.1%) missing values Missing
Capacity has 10745 (25.1%) missing values Missing
ExamDurationMinute has 10745 (25.1%) missing values Missing
IsResultImmediately has 10745 (25.1%) missing values Missing
PageSize has 10745 (25.1%) missing values Missing
ShiftName has 10745 (25.1%) missing values Missing
StartTime has 10745 (25.1%) missing values Missing
EndTime has 10745 (25.1%) missing values Missing
FormStatus has 10741 (25.1%) missing values Missing
HasStudentAttemptedExam has 10741 (25.1%) missing values Missing
FormIndex has 10743 (25.1%) missing values Missing
FacultyID has 10745 (25.1%) missing values Missing
LevelID has 10745 (25.1%) missing values Missing
ShiftID has 10745 (25.1%) missing values Missing
FiscalYearID has 10745 (25.1%) missing values Missing
FacultyName has 10745 (25.1%) missing values Missing
LevelName has 10745 (25.1%) missing values Missing
FiscalYearName has 10745 (25.1%) missing values Missing
RollNoString has 42757 (100.0%) missing values Missing
PhotoDocumentID has 10741 (25.1%) missing values Missing
ExamRollNo has 42757 (100.0%) missing values Missing
Active has 10741 (25.1%) missing values Missing
ExamStartedTime has 11569 (27.1%) missing values Missing
CreatedBy has 10741 (25.1%) missing values Missing
CreatedDate has 10741 (25.1%) missing values Missing
ModifiedBy has 35537 (83.1%) missing values Missing
ModifiedDate has 35537 (83.1%) missing values Missing
SLCBoardName has 30049 (70.3%) missing values Missing
PCLBoardName has 30049 (70.3%) missing values Missing
ContactNo is highly skewed (γ1 = 22.46132012) Skewed
FormNo is uniformly distributed Uniform
FullName is uniformly distributed Uniform
StudentCode is uniformly distributed Uniform
EthnicGroupSpecify is uniformly distributed Uniform
SLCSchoolFullAddress is uniformly distributed Uniform
Password is uniformly distributed Uniform
BirthDateBS is an unsupported type, check if it needs cleaning or further analysis Unsupported
BirthDateAD is an unsupported type, check if it needs cleaning or further analysis Unsupported
SLCSymbolNo is an unsupported type, check if it needs cleaning or further analysis Unsupported
PCLSymbolNo is an unsupported type, check if it needs cleaning or further analysis Unsupported
VoucherNo is an unsupported type, check if it needs cleaning or further analysis Unsupported
IndianEmbassyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
SLCEquivalentSpecify is an unsupported type, check if it needs cleaning or further analysis Unsupported
PCLEquivalentSpecify is an unsupported type, check if it needs cleaning or further analysis Unsupported
IdentificationNo is an unsupported type, check if it needs cleaning or further analysis Unsupported
FormSubmittedDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
CheckedBy is an unsupported type, check if it needs cleaning or further analysis Unsupported
ExamSessionDateAD is an unsupported type, check if it needs cleaning or further analysis Unsupported
ExamSessionDateBS is an unsupported type, check if it needs cleaning or further analysis Unsupported
StartTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
EndTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
RollNoString is an unsupported type, check if it needs cleaning or further analysis Unsupported
ExamRollNo is an unsupported type, check if it needs cleaning or further analysis Unsupported
ExamStartedTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
CreatedDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
ModifiedDate is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-05-15 04:29:49.937221
Analysis finished2021-05-15 04:31:13.072389
Duration1 minute and 23.14 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

FormNo
Categorical

HIGH CARDINALITY
UNIFORM

Distinct42755
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Memory size334.2 KiB
2077-6238
 
1
2076-9852
 
1
2077-4138
 
1
2074-9496
 
1
2077-1191
 
1
Other values (42750)
42750 

Length

Max length10
Median length9
Mean length9.056227342
Min length6

Characters and Unicode

Total characters387199
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42755 ?
Unique (%)100.0%

Sample

1st row2073-1
2nd row2073-2
3rd row2073-3
4th row2073-4
5th row2073-5

Common Values

ValueCountFrequency (%)
2077-62381
 
< 0.1%
2076-98521
 
< 0.1%
2077-41381
 
< 0.1%
2074-94961
 
< 0.1%
2077-11911
 
< 0.1%
2076-100281
 
< 0.1%
2077-76171
 
< 0.1%
2074-55811
 
< 0.1%
2074-82051
 
< 0.1%
2076-73821
 
< 0.1%
Other values (42745)42745
> 99.9%
(Missing)2
 
< 0.1%

Length

2021-05-15T10:16:13.284501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2077-9641
 
< 0.1%
2077-62381
 
< 0.1%
2076-98521
 
< 0.1%
2077-41381
 
< 0.1%
2074-94961
 
< 0.1%
2077-11911
 
< 0.1%
2076-100281
 
< 0.1%
2077-76171
 
< 0.1%
2074-55811
 
< 0.1%
2074-82051
 
< 0.1%
Other values (42745)42745
> 99.9%

Most occurring characters

ValueCountFrequency (%)
771802
18.5%
260584
15.6%
058439
15.1%
-42755
11.0%
428827
7.4%
327331
 
7.1%
125642
 
6.6%
623347
 
6.0%
516417
 
4.2%
816055
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number344444
89.0%
Dash Punctuation42755
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
771802
20.8%
260584
17.6%
058439
17.0%
428827
8.4%
327331
 
7.9%
125642
 
7.4%
623347
 
6.8%
516417
 
4.8%
816055
 
4.7%
916000
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
-42755
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common387199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
771802
18.5%
260584
15.6%
058439
15.1%
-42755
11.0%
428827
7.4%
327331
 
7.1%
125642
 
6.6%
623347
 
6.0%
516417
 
4.2%
816055
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII387199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
771802
18.5%
260584
15.6%
058439
15.1%
-42755
11.0%
428827
7.4%
327331
 
7.1%
125642
 
6.6%
623347
 
6.0%
516417
 
4.2%
816055
 
4.1%

FirstName
Categorical

HIGH CARDINALITY

Distinct10332
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Bibek
 
335
Roshan
 
301
Sagar
 
253
Bishal
 
242
Manish
 
238
Other values (10327)
41388 

Length

Max length24
Median length6
Mean length6.041817714
Min length2

Characters and Unicode

Total characters258330
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6533 ?
Unique (%)15.3%

Sample

1st rowAashutosh
2nd rowBikal
3rd rowShramik
4th rowSHISHIR
5th rowdurga

Common Values

ValueCountFrequency (%)
Bibek335
 
0.8%
Roshan301
 
0.7%
Sagar253
 
0.6%
Bishal242
 
0.6%
Manish238
 
0.6%
Santosh216
 
0.5%
Suman212
 
0.5%
Nabin212
 
0.5%
Sandesh203
 
0.5%
Abhishek200
 
0.5%
Other values (10322)40345
94.4%

Length

2021-05-15T10:16:13.515060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bibek469
 
1.1%
roshan380
 
0.9%
sagar343
 
0.8%
santosh328
 
0.8%
manish328
 
0.8%
bishal326
 
0.8%
nabin284
 
0.7%
suman283
 
0.7%
sujan275
 
0.6%
suraj275
 
0.6%
Other values (6852)39547
92.3%

Most occurring characters

ValueCountFrequency (%)
a37242
 
14.4%
i19316
 
7.5%
h17471
 
6.8%
n15585
 
6.0%
s14184
 
5.5%
A13156
 
5.1%
S12581
 
4.9%
r11380
 
4.4%
e8320
 
3.2%
u8055
 
3.1%
Other values (45)101040
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter182608
70.7%
Uppercase Letter72688
 
28.1%
Space Separator2995
 
1.2%
Other Punctuation38
 
< 0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A13156
18.1%
S12581
17.3%
R6025
8.3%
N4937
 
6.8%
B4359
 
6.0%
P4083
 
5.6%
I3650
 
5.0%
H3460
 
4.8%
M3061
 
4.2%
D2805
 
3.9%
Other values (16)14571
20.0%
Lowercase Letter
ValueCountFrequency (%)
a37242
20.4%
i19316
10.6%
h17471
9.6%
n15585
 
8.5%
s14184
 
7.8%
r11380
 
6.2%
e8320
 
4.6%
u8055
 
4.4%
t6114
 
3.3%
m5642
 
3.1%
Other values (16)39299
21.5%
Space Separator
ValueCountFrequency (%)
2995
100.0%
Other Punctuation
ValueCountFrequency (%)
.38
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin255296
98.8%
Common3034
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a37242
 
14.6%
i19316
 
7.6%
h17471
 
6.8%
n15585
 
6.1%
s14184
 
5.6%
A13156
 
5.2%
S12581
 
4.9%
r11380
 
4.5%
e8320
 
3.3%
u8055
 
3.2%
Other values (42)98006
38.4%
Common
ValueCountFrequency (%)
2995
98.7%
.38
 
1.3%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII258330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a37242
 
14.4%
i19316
 
7.5%
h17471
 
6.8%
n15585
 
6.0%
s14184
 
5.5%
A13156
 
5.1%
S12581
 
4.9%
r11380
 
4.4%
e8320
 
3.2%
u8055
 
3.1%
Other values (45)101040
39.1%

MiddleName
Categorical

HIGH CARDINALITY
MISSING

Distinct1014
Distinct (%)11.2%
Missing33685
Missing (%)78.8%
Memory size334.2 KiB
Kumar
2635 
KUMAR
707 
Raj
568 
kumar
 
405
Prasad
 
390
Other values (1009)
4367 

Length

Max length34
Median length5
Mean length5.226741623
Min length1

Characters and Unicode

Total characters47417
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique696 ?
Unique (%)7.7%

Sample

1st rowraj
2nd rowSagar
3rd rowKUMAR
4th rowKhadka
5th rowRam

Common Values

ValueCountFrequency (%)
Kumar2635
 
6.2%
KUMAR707
 
1.7%
Raj568
 
1.3%
kumar405
 
0.9%
Prasad390
 
0.9%
Bahadur314
 
0.7%
Kumar 173
 
0.4%
Singh161
 
0.4%
Kumari154
 
0.4%
RAJ120
 
0.3%
Other values (1004)3445
 
8.1%
(Missing)33685
78.8%

Length

2021-05-15T10:16:13.739316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kumar4003
43.9%
raj779
 
8.5%
prasad577
 
6.3%
bahadur442
 
4.8%
singh237
 
2.6%
kumari231
 
2.5%
thapa127
 
1.4%
prakash117
 
1.3%
narayan111
 
1.2%
bikram104
 
1.1%
Other values (678)2399
26.3%

Most occurring characters

ValueCountFrequency (%)
a8800
18.6%
r5329
11.2%
u4206
 
8.9%
K4109
 
8.7%
m3846
 
8.1%
R2118
 
4.5%
A1959
 
4.1%
h1631
 
3.4%
n1142
 
2.4%
d1117
 
2.4%
Other values (44)13160
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31689
66.8%
Uppercase Letter15047
31.7%
Space Separator664
 
1.4%
Other Punctuation11
 
< 0.1%
Dash Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8800
27.8%
r5329
16.8%
u4206
13.3%
m3846
12.1%
h1631
 
5.1%
n1142
 
3.6%
d1117
 
3.5%
i1017
 
3.2%
s879
 
2.8%
k820
 
2.6%
Other values (16)2902
 
9.2%
Uppercase Letter
ValueCountFrequency (%)
K4109
27.3%
R2118
14.1%
A1959
13.0%
M1031
 
6.9%
U988
 
6.6%
P819
 
5.4%
B776
 
5.2%
S764
 
5.1%
D402
 
2.7%
N389
 
2.6%
Other values (15)1692
11.2%
Space Separator
ValueCountFrequency (%)
664
100.0%
Other Punctuation
ValueCountFrequency (%)
.11
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin46736
98.6%
Common681
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8800
18.8%
r5329
11.4%
u4206
 
9.0%
K4109
 
8.8%
m3846
 
8.2%
R2118
 
4.5%
A1959
 
4.2%
h1631
 
3.5%
n1142
 
2.4%
d1117
 
2.4%
Other values (41)12479
26.7%
Common
ValueCountFrequency (%)
664
97.5%
.11
 
1.6%
-6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII47417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a8800
18.6%
r5329
11.2%
u4206
 
8.9%
K4109
 
8.7%
m3846
 
8.1%
R2118
 
4.5%
A1959
 
4.1%
h1631
 
3.4%
n1142
 
2.4%
d1117
 
2.4%
Other values (44)13160
27.8%

LastName
Categorical

HIGH CARDINALITY

Distinct3287
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Shrestha
 
1852
Yadav
 
1304
Adhikari
 
841
Sah
 
833
Chaudhary
 
721
Other values (3282)
37206 

Length

Max length20
Median length6
Mean length6.196131628
Min length2

Characters and Unicode

Total characters264928
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1649 ?
Unique (%)3.9%

Sample

1st rowAdhikari
2nd rowYonjan
3rd rowRimal
4th rowKHADKA
5th rownepal

Common Values

ValueCountFrequency (%)
Shrestha1852
 
4.3%
Yadav1304
 
3.0%
Adhikari841
 
2.0%
Sah833
 
1.9%
Chaudhary721
 
1.7%
Thapa708
 
1.7%
Poudel550
 
1.3%
Bhattarai500
 
1.2%
Karki495
 
1.2%
Bhandari471
 
1.1%
Other values (3277)34482
80.6%

Length

2021-05-15T10:16:13.984984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shrestha2358
 
5.5%
yadav1858
 
4.3%
sah1217
 
2.8%
adhikari1084
 
2.5%
chaudhary1022
 
2.4%
thapa964
 
2.2%
poudel704
 
1.6%
karki648
 
1.5%
bhattarai605
 
1.4%
bhandari595
 
1.4%
Other values (1820)32088
74.4%

Most occurring characters

ValueCountFrequency (%)
a50000
18.9%
h23307
 
8.8%
r14475
 
5.5%
i14466
 
5.5%
A11002
 
4.2%
e9254
 
3.5%
t8971
 
3.4%
d8625
 
3.3%
l8405
 
3.2%
n8334
 
3.1%
Other values (47)108089
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter190385
71.9%
Uppercase Letter72963
 
27.5%
Space Separator912
 
0.3%
Other Punctuation655
 
0.2%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A11002
15.1%
S7745
 
10.6%
K5140
 
7.0%
R4436
 
6.1%
P4411
 
6.0%
B4286
 
5.9%
H4134
 
5.7%
T3934
 
5.4%
D3785
 
5.2%
M3166
 
4.3%
Other values (16)20924
28.7%
Lowercase Letter
ValueCountFrequency (%)
a50000
26.3%
h23307
12.2%
r14475
 
7.6%
i14466
 
7.6%
e9254
 
4.9%
t8971
 
4.7%
d8625
 
4.5%
l8405
 
4.4%
n8334
 
4.4%
u7974
 
4.2%
Other values (16)36574
19.2%
Other Punctuation
ValueCountFrequency (%)
.655
100.0%
Space Separator
ValueCountFrequency (%)
912
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin263348
99.4%
Common1580
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a50000
19.0%
h23307
 
8.9%
r14475
 
5.5%
i14466
 
5.5%
A11002
 
4.2%
e9254
 
3.5%
t8971
 
3.4%
d8625
 
3.3%
l8405
 
3.2%
n8334
 
3.2%
Other values (42)106509
40.4%
Common
ValueCountFrequency (%)
912
57.7%
.655
41.5%
(6
 
0.4%
)6
 
0.4%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII264928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a50000
18.9%
h23307
 
8.8%
r14475
 
5.5%
i14466
 
5.5%
A11002
 
4.2%
e9254
 
3.5%
t8971
 
3.4%
d8625
 
3.3%
l8405
 
3.2%
n8334
 
3.1%
Other values (47)108089
40.8%

MunicipalityVdc
Categorical

HIGH CARDINALITY

Distinct10899
Distinct (%)25.5%
Missing41
Missing (%)0.1%
Memory size334.2 KiB
Kathmandu
 
850
Biratnagar
 
517
Pokhara
 
474
Bharatpur
 
449
Bhaktapur
 
418
Other values (10894)
40008 

Length

Max length43
Median length9
Mean length9.801432718
Min length2

Characters and Unicode

Total characters418678
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6652 ?
Unique (%)15.6%

Sample

1st rowChitrawan
2nd rowBishnupurkatti
3rd rowBidur
4th rowBhimeshwor
5th rowprangbung

Common Values

ValueCountFrequency (%)
Kathmandu850
 
2.0%
Biratnagar517
 
1.2%
Pokhara474
 
1.1%
Bharatpur449
 
1.1%
Bhaktapur418
 
1.0%
Lalitpur358
 
0.8%
Dharan275
 
0.6%
Itahari258
 
0.6%
Hetauda255
 
0.6%
Butwal244
 
0.6%
Other values (10889)38618
90.3%

Length

2021-05-15T10:16:14.249424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kathmandu1492
 
3.0%
municipality1365
 
2.8%
pokhara976
 
2.0%
biratnagar686
 
1.4%
bharatpur680
 
1.4%
lalitpur659
 
1.3%
bhaktapur587
 
1.2%
metropolitan471
 
1.0%
city416
 
0.8%
dharan398
 
0.8%
Other values (5911)41397
84.3%

Most occurring characters

ValueCountFrequency (%)
a78031
18.6%
i30630
 
7.3%
h28361
 
6.8%
r27825
 
6.6%
n22663
 
5.4%
u20630
 
4.9%
t18010
 
4.3%
l12344
 
2.9%
d11212
 
2.7%
p10979
 
2.6%
Other values (45)157993
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter335065
80.0%
Uppercase Letter74798
 
17.9%
Space Separator8013
 
1.9%
Other Punctuation411
 
0.1%
Dash Punctuation391
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a78031
23.3%
i30630
 
9.1%
h28361
 
8.5%
r27825
 
8.3%
n22663
 
6.8%
u20630
 
6.2%
t18010
 
5.4%
l12344
 
3.7%
d11212
 
3.3%
p10979
 
3.3%
Other values (16)74380
22.2%
Uppercase Letter
ValueCountFrequency (%)
A9022
12.1%
B8308
 
11.1%
M5606
 
7.5%
K5161
 
6.9%
R5048
 
6.7%
S4244
 
5.7%
D3961
 
5.3%
H3955
 
5.3%
T3926
 
5.2%
P3759
 
5.0%
Other values (15)21808
29.2%
Other Punctuation
ValueCountFrequency (%)
.410
99.8%
&1
 
0.2%
Space Separator
ValueCountFrequency (%)
8013
100.0%
Dash Punctuation
ValueCountFrequency (%)
-391
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin409863
97.9%
Common8815
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a78031
19.0%
i30630
 
7.5%
h28361
 
6.9%
r27825
 
6.8%
n22663
 
5.5%
u20630
 
5.0%
t18010
 
4.4%
l12344
 
3.0%
d11212
 
2.7%
p10979
 
2.7%
Other values (41)149178
36.4%
Common
ValueCountFrequency (%)
8013
90.9%
.410
 
4.7%
-391
 
4.4%
&1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII418678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a78031
18.6%
i30630
 
7.3%
h28361
 
6.8%
r27825
 
6.6%
n22663
 
5.4%
u20630
 
4.9%
t18010
 
4.3%
l12344
 
2.9%
d11212
 
2.7%
p10979
 
2.6%
Other values (45)157993
37.7%

WardNo
Real number (ℝ≥0)

Distinct36
Distinct (%)0.1%
Missing41
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.032376627
Minimum0
Maximum35
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:14.360570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile18
Maximum35
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.654458003
Coefficient of variation (CV)0.8040607469
Kurtosis4.790221938
Mean7.032376627
Median Absolute Deviation (MAD)3
Skewness1.908107436
Sum300395
Variance31.97289531
MonotonicityNot monotonic
2021-05-15T10:16:14.461565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
24147
9.7%
44133
9.7%
14064
9.5%
53979
9.3%
33888
9.1%
63774
8.8%
73558
8.3%
83234
7.6%
92978
 
7.0%
101429
 
3.3%
Other values (26)7532
17.6%
ValueCountFrequency (%)
01
 
< 0.1%
14064
9.5%
24147
9.7%
33888
9.1%
44133
9.7%
53979
9.3%
63774
8.8%
73558
8.3%
83234
7.6%
92978
7.0%
ValueCountFrequency (%)
3562
0.1%
3456
 
0.1%
3348
 
0.1%
32128
0.3%
3199
0.2%
3055
 
0.1%
2976
0.2%
2873
0.2%
27102
0.2%
26148
0.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Male
35019 
Female
7738 

Length

Max length6
Median length4
Mean length4.361952429
Min length4

Characters and Unicode

Total characters186504
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male35019
81.9%
Female7738
 
18.1%

Length

2021-05-15T10:16:14.655397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:14.726075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
male35019
81.9%
female7738
 
18.1%

Most occurring characters

ValueCountFrequency (%)
e50495
27.1%
a42757
22.9%
l42757
22.9%
M35019
18.8%
F7738
 
4.1%
m7738
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter143747
77.1%
Uppercase Letter42757
 
22.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e50495
35.1%
a42757
29.7%
l42757
29.7%
m7738
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
M35019
81.9%
F7738
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
Latin186504
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e50495
27.1%
a42757
22.9%
l42757
22.9%
M35019
18.8%
F7738
 
4.1%
m7738
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII186504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e50495
27.1%
a42757
22.9%
l42757
22.9%
M35019
18.8%
F7738
 
4.1%
m7738
 
4.1%

BirthDateBS
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size334.2 KiB

BirthDateAD
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size334.2 KiB

SLCSchoolName
Categorical

HIGH CARDINALITY

Distinct26224
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Siddhartha Vanasthali Institute
 
131
Sainik Awasiya Mahavidyalaya
 
106
GEMS
 
99
Galaxy Public School
 
94
Little Angels' School
 
85
Other values (26219)
42242 

Length

Max length93
Median length27
Mean length27.34366303
Min length3

Characters and Unicode

Total characters1169133
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20981 ?
Unique (%)49.1%

Sample

1st rowTriyog Higher Secondary School
2nd rowGreenland English Boarding School
3rd rowBagmati Boarding H.S. School
4th rowSainik Awasiya Mahavidhyalaya
5th rowbud international public school

Common Values

ValueCountFrequency (%)
Siddhartha Vanasthali Institute131
 
0.3%
Sainik Awasiya Mahavidyalaya106
 
0.2%
GEMS99
 
0.2%
Galaxy Public School94
 
0.2%
Little Angels' School85
 
0.2%
Budhanilkantha School78
 
0.2%
Occidental Public School77
 
0.2%
Aishwarya Vidya Niketan76
 
0.2%
Gandaki Boarding School74
 
0.2%
Paragon Public School72
 
0.2%
Other values (26214)41865
97.9%

Length

2021-05-15T10:16:14.938176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
school26888
 
16.5%
secondary13102
 
8.0%
english7599
 
4.7%
boarding6841
 
4.2%
shree5992
 
3.7%
higher4476
 
2.7%
academy3503
 
2.1%
ma3156
 
1.9%
vi3135
 
1.9%
public1842
 
1.1%
Other values (6853)86725
53.1%

Most occurring characters

ValueCountFrequency (%)
123829
 
10.6%
a101213
 
8.7%
o83273
 
7.1%
h64952
 
5.6%
S60384
 
5.2%
i58834
 
5.0%
e57313
 
4.9%
n57292
 
4.9%
r53713
 
4.6%
l50534
 
4.3%
Other values (47)457796
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter781053
66.8%
Uppercase Letter249113
 
21.3%
Space Separator123829
 
10.6%
Other Punctuation14887
 
1.3%
Dash Punctuation251
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S60384
24.2%
A22077
 
8.9%
H18763
 
7.5%
E16959
 
6.8%
B13168
 
5.3%
M12029
 
4.8%
N11723
 
4.7%
I10749
 
4.3%
O10169
 
4.1%
R9292
 
3.7%
Other values (16)63800
25.6%
Lowercase Letter
ValueCountFrequency (%)
a101213
13.0%
o83273
10.7%
h64952
 
8.3%
i58834
 
7.5%
e57313
 
7.3%
n57292
 
7.3%
r53713
 
6.9%
l50534
 
6.5%
c46109
 
5.9%
d39558
 
5.1%
Other values (16)168262
21.5%
Other Punctuation
ValueCountFrequency (%)
.13726
92.2%
'1153
 
7.7%
&8
 
0.1%
Space Separator
ValueCountFrequency (%)
123829
100.0%
Dash Punctuation
ValueCountFrequency (%)
-251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1030166
88.1%
Common138967
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a101213
 
9.8%
o83273
 
8.1%
h64952
 
6.3%
S60384
 
5.9%
i58834
 
5.7%
e57313
 
5.6%
n57292
 
5.6%
r53713
 
5.2%
l50534
 
4.9%
c46109
 
4.5%
Other values (42)396549
38.5%
Common
ValueCountFrequency (%)
123829
89.1%
.13726
 
9.9%
'1153
 
0.8%
-251
 
0.2%
&8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1169133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123829
 
10.6%
a101213
 
8.7%
o83273
 
7.1%
h64952
 
5.6%
S60384
 
5.2%
i58834
 
5.0%
e57313
 
4.9%
n57292
 
4.9%
r53713
 
4.6%
l50534
 
4.3%
Other values (47)457796
39.2%

SLCSymbolNo
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size334.2 KiB

SLCPassedYear
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2066.933602
Minimum1999
Maximum2077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:15.523434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2017
Q12070
median2071
Q32073
95-th percentile2074
Maximum2077
Range78
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.51308865
Coefficient of variation (CV)0.007505363811
Kurtosis6.885560386
Mean2066.933602
Median Absolute Deviation (MAD)2
Skewness-2.947056658
Sum88375880
Variance240.6559195
MonotonicityNot monotonic
2021-05-15T10:16:15.636517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
20709178
21.5%
20748994
21.0%
20718839
20.7%
20736453
15.1%
20692393
 
5.6%
20721620
 
3.8%
2018947
 
2.2%
2017883
 
2.1%
2068735
 
1.7%
2015528
 
1.2%
Other values (37)2187
 
5.1%
ValueCountFrequency (%)
19991
 
< 0.1%
20011
 
< 0.1%
20021
 
< 0.1%
20051
 
< 0.1%
20064
 
< 0.1%
20073
 
< 0.1%
20083
 
< 0.1%
20093
 
< 0.1%
201028
0.1%
201145
0.1%
ValueCountFrequency (%)
20775
 
< 0.1%
207621
 
< 0.1%
2075226
 
0.5%
20748994
21.0%
20736453
15.1%
20721620
 
3.8%
20718839
20.7%
20709178
21.5%
20692393
 
5.6%
2068735
 
1.7%

SLCPassedYearCalendar
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
BS
39230 
AD
 
3527

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters85514
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBS
2nd rowBS
3rd rowBS
4th rowBS
5th rowBS

Common Values

ValueCountFrequency (%)
BS39230
91.8%
AD3527
 
8.2%

Length

2021-05-15T10:16:15.838522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:15.899181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bs39230
91.8%
ad3527
 
8.2%

Most occurring characters

ValueCountFrequency (%)
B39230
45.9%
S39230
45.9%
A3527
 
4.1%
D3527
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter85514
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B39230
45.9%
S39230
45.9%
A3527
 
4.1%
D3527
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin85514
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B39230
45.9%
S39230
45.9%
A3527
 
4.1%
D3527
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII85514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B39230
45.9%
S39230
45.9%
A3527
 
4.1%
D3527
 
4.1%

SLCPercentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1247
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.08230044
Minimum1.85
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:15.969892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile3.15
Q13.6
median63.63
Q379.63
95-th percentile87.13
Maximum97
Range95.15
Interquartile range (IQR)76.03

Descriptive statistics

Standard deviation37.01112465
Coefficient of variation (CV)0.8395914977
Kurtosis-1.88335469
Mean44.08230044
Median Absolute Deviation (MAD)23.25
Skewness-0.1252770712
Sum1884826.92
Variance1369.823348
MonotonicityNot monotonic
2021-05-15T10:16:16.091060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.651614
 
3.8%
3.61559
 
3.6%
3.71554
 
3.6%
3.751533
 
3.6%
3.551331
 
3.1%
3.81311
 
3.1%
3.51196
 
2.8%
3.451096
 
2.6%
3.4930
 
2.2%
3.85856
 
2.0%
Other values (1237)29777
69.6%
ValueCountFrequency (%)
1.851
 
< 0.1%
21
 
< 0.1%
2.051
 
< 0.1%
2.11
 
< 0.1%
2.153
< 0.1%
2.191
 
< 0.1%
2.21
 
< 0.1%
2.231
 
< 0.1%
2.256
< 0.1%
2.36
< 0.1%
ValueCountFrequency (%)
973
 
< 0.1%
96.251
 
< 0.1%
961
 
< 0.1%
95.21
 
< 0.1%
9511
< 0.1%
94.131
 
< 0.1%
944
 
< 0.1%
93.51
 
< 0.1%
93.41
 
< 0.1%
93.131
 
< 0.1%

PCLSymbolNo
Unsupported

REJECTED
UNSUPPORTED

Missing1
Missing (%)< 0.1%
Memory size334.2 KiB

PCLSchoolName
Categorical

HIGH CARDINALITY

Distinct15347
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Kathmandu Model Secondary School
 
418
St. Xavier's College
 
389
Trinity International College
 
381
United Academy
 
352
Prasadi Academy
 
305
Other values (15342)
40912 

Length

Max length77
Median length26
Mean length26.08382253
Min length3

Characters and Unicode

Total characters1115266
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10701 ?
Unique (%)25.0%

Sample

1st rowLiverpool International College
2nd rowThe New Summit H.S School
3rd rowKathmandu Model H.S. School
4th rowSainik Awasiya Mahavidhyalaya H S School
5th rowCOHED

Common Values

ValueCountFrequency (%)
Kathmandu Model Secondary School418
 
1.0%
St. Xavier's College389
 
0.9%
Trinity International College381
 
0.9%
United Academy352
 
0.8%
Prasadi Academy305
 
0.7%
Kathmandu Model College299
 
0.7%
Nepal Mega College235
 
0.5%
Capital Secondary School225
 
0.5%
National School of Sciences206
 
0.5%
Arniko Awasiya Secondary School199
 
0.5%
Other values (15337)39748
93.0%

Length

2021-05-15T10:16:16.373935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
school19782
 
13.3%
secondary14424
 
9.7%
college9480
 
6.4%
international4998
 
3.4%
higher4715
 
3.2%
academy3196
 
2.2%
model2595
 
1.7%
kathmandu2300
 
1.6%
s2235
 
1.5%
of2227
 
1.5%
Other values (2993)82383
55.5%

Most occurring characters

ValueCountFrequency (%)
108967
 
9.8%
a87405
 
7.8%
o83973
 
7.5%
e77870
 
7.0%
n62107
 
5.6%
l61669
 
5.5%
S52079
 
4.7%
i47938
 
4.3%
c45674
 
4.1%
r44985
 
4.0%
Other values (47)442599
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter749301
67.2%
Uppercase Letter241591
 
21.7%
Space Separator108967
 
9.8%
Other Punctuation15336
 
1.4%
Dash Punctuation71
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S52079
21.6%
A20364
 
8.4%
C17550
 
7.3%
H17457
 
7.2%
I13815
 
5.7%
E13477
 
5.6%
N13320
 
5.5%
O12563
 
5.2%
L10847
 
4.5%
M9782
 
4.0%
Other values (16)60337
25.0%
Lowercase Letter
ValueCountFrequency (%)
a87405
11.7%
o83973
11.2%
e77870
10.4%
n62107
 
8.3%
l61669
 
8.2%
i47938
 
6.4%
c45674
 
6.1%
r44985
 
6.0%
h42776
 
5.7%
t35509
 
4.7%
Other values (16)159395
21.3%
Other Punctuation
ValueCountFrequency (%)
.12949
84.4%
'2313
 
15.1%
&74
 
0.5%
Space Separator
ValueCountFrequency (%)
108967
100.0%
Dash Punctuation
ValueCountFrequency (%)
-71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin990892
88.8%
Common124374
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a87405
 
8.8%
o83973
 
8.5%
e77870
 
7.9%
n62107
 
6.3%
l61669
 
6.2%
S52079
 
5.3%
i47938
 
4.8%
c45674
 
4.6%
r44985
 
4.5%
h42776
 
4.3%
Other values (42)384416
38.8%
Common
ValueCountFrequency (%)
108967
87.6%
.12949
 
10.4%
'2313
 
1.9%
&74
 
0.1%
-71
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1115266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108967
 
9.8%
a87405
 
7.8%
o83973
 
7.5%
e77870
 
7.0%
n62107
 
5.6%
l61669
 
5.5%
S52079
 
4.7%
i47938
 
4.3%
c45674
 
4.1%
r44985
 
4.0%
Other values (47)442599
39.7%

PCLPercentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1473
Distinct (%)15.7%
Missing33398
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean48.56978096
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:16.503139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.76
Q13.49
median61.7
Q370.905
95-th percentile80.4
Maximum100
Range100
Interquartile range (IQR)67.415

Descriptive statistics

Standard deviation29.99127834
Coefficient of variation (CV)0.6174884413
Kurtosis-1.204419297
Mean48.56978096
Median Absolute Deviation (MAD)11.2
Skewness-0.7239369793
Sum454564.58
Variance899.4767763
MonotonicityNot monotonic
2021-05-15T10:16:16.616198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6055
 
0.1%
6550
 
0.1%
3.2348
 
0.1%
3.1947
 
0.1%
2.9545
 
0.1%
7045
 
0.1%
6243
 
0.1%
3.1642
 
0.1%
6942
 
0.1%
3.0341
 
0.1%
Other values (1463)8901
 
20.8%
(Missing)33398
78.1%
ValueCountFrequency (%)
01
 
< 0.1%
21
 
< 0.1%
2.131
 
< 0.1%
2.191
 
< 0.1%
2.251
 
< 0.1%
2.282
 
< 0.1%
2.35
< 0.1%
2.311
 
< 0.1%
2.321
 
< 0.1%
2.341
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
95.61
< 0.1%
951
< 0.1%
94.42
< 0.1%
93.61
< 0.1%
93.331
< 0.1%
931
< 0.1%
92.82
< 0.1%
92.21
< 0.1%
921
< 0.1%

PCLPassedYear
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct45
Distinct (%)0.5%
Missing33287
Missing (%)77.9%
Infinite0
Infinite (%)0.0%
Mean2062.493559
Minimum1997
Maximum2077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:16.735402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2015
Q12071
median2072
Q32075
95-th percentile2076
Maximum2077
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.31863245
Coefficient of variation (CV)0.01082118893
Kurtosis0.484529046
Mean2062.493559
Median Absolute Deviation (MAD)2
Skewness-1.559377476
Sum19531814
Variance498.1213546
MonotonicityNot monotonic
2021-05-15T10:16:16.848526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20722309
 
5.4%
20731913
 
4.5%
20761436
 
3.4%
20751027
 
2.4%
2071433
 
1.0%
2016410
 
1.0%
2019397
 
0.9%
2015333
 
0.8%
2018246
 
0.6%
2074219
 
0.5%
Other values (35)747
 
1.7%
(Missing)33287
77.9%
ValueCountFrequency (%)
19971
 
< 0.1%
19981
 
< 0.1%
20021
 
< 0.1%
20032
 
< 0.1%
20042
 
< 0.1%
20051
 
< 0.1%
20061
 
< 0.1%
20073
< 0.1%
20085
< 0.1%
20095
< 0.1%
ValueCountFrequency (%)
207727
 
0.1%
20761436
3.4%
20751027
2.4%
2074219
 
0.5%
20731913
4.5%
20722309
5.4%
2071433
 
1.0%
2070152
 
0.4%
206959
 
0.1%
206834
 
0.1%

PCLPassedYearCalendar
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
BS
40952 
AD
 
1805

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters85514
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBS
2nd rowBS
3rd rowBS
4th rowBS
5th rowBS

Common Values

ValueCountFrequency (%)
BS40952
95.8%
AD1805
 
4.2%

Length

2021-05-15T10:16:17.060628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:17.119147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bs40952
95.8%
ad1805
 
4.2%

Most occurring characters

ValueCountFrequency (%)
B40952
47.9%
S40952
47.9%
A1805
 
2.1%
D1805
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter85514
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B40952
47.9%
S40952
47.9%
A1805
 
2.1%
D1805
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin85514
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B40952
47.9%
S40952
47.9%
A1805
 
2.1%
D1805
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII85514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B40952
47.9%
S40952
47.9%
A1805
 
2.1%
D1805
 
2.1%

EthnicGroupID
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)28.6%
Missing42750
Missing (%)> 99.9%
Memory size334.2 KiB
2.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.05
 
< 0.1%
3.02
 
< 0.1%
(Missing)42750
> 99.9%

Length

2021-05-15T10:16:17.262591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:17.313122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.05
71.4%
3.02
 
28.6%

Most occurring characters

ValueCountFrequency (%)
.7
33.3%
07
33.3%
25
23.8%
32
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14
66.7%
Other Punctuation7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07
50.0%
25
35.7%
32
 
14.3%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.7
33.3%
07
33.3%
25
23.8%
32
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.7
33.3%
07
33.3%
25
23.8%
32
 
9.5%

FatherFirstName
Categorical

HIGH CARDINALITY

Distinct9331
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Ram
 
1148
Krishna
 
605
Hari
 
376
Bishnu
 
350
Narayan
 
302
Other values (9326)
39976 

Length

Max length22
Median length6
Mean length5.945997147
Min length1

Characters and Unicode

Total characters254233
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5942 ?
Unique (%)13.9%

Sample

1st rowPitri
2nd rowBhailal
3rd rowKrishna
4th rowShiva
5th rowdevi

Common Values

ValueCountFrequency (%)
Ram1148
 
2.7%
Krishna605
 
1.4%
Hari376
 
0.9%
Bishnu350
 
0.8%
Narayan302
 
0.7%
Rajendra280
 
0.7%
Ram 266
 
0.6%
RAM253
 
0.6%
Prem242
 
0.6%
Ramesh232
 
0.5%
Other values (9321)38703
90.5%

Length

2021-05-15T10:16:17.513066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram1923
 
4.4%
krishna1035
 
2.4%
hari631
 
1.5%
bishnu571
 
1.3%
narayan512
 
1.2%
rajendra413
 
1.0%
prem383
 
0.9%
shyam379
 
0.9%
ramesh355
 
0.8%
ganesh349
 
0.8%
Other values (5493)36695
84.9%

Most occurring characters

ValueCountFrequency (%)
a41747
 
16.4%
r16004
 
6.3%
h15811
 
6.2%
n14525
 
5.7%
i12581
 
4.9%
A9275
 
3.6%
m8770
 
3.4%
e8730
 
3.4%
R8418
 
3.3%
d7749
 
3.0%
Other values (44)110623
43.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter173672
68.3%
Uppercase Letter72826
28.6%
Space Separator7676
 
3.0%
Other Punctuation56
 
< 0.1%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a41747
24.0%
r16004
 
9.2%
h15811
 
9.1%
n14525
 
8.4%
i12581
 
7.2%
m8770
 
5.0%
e8730
 
5.0%
d7749
 
4.5%
s7305
 
4.2%
u7154
 
4.1%
Other values (16)33296
19.2%
Uppercase Letter
ValueCountFrequency (%)
A9275
12.7%
R8418
11.6%
S6030
 
8.3%
B5433
 
7.5%
D4737
 
6.5%
N4651
 
6.4%
H4322
 
5.9%
K4093
 
5.6%
M4092
 
5.6%
I2823
 
3.9%
Other values (15)18952
26.0%
Space Separator
ValueCountFrequency (%)
7676
100.0%
Other Punctuation
ValueCountFrequency (%)
.56
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin246498
97.0%
Common7735
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a41747
16.9%
r16004
 
6.5%
h15811
 
6.4%
n14525
 
5.9%
i12581
 
5.1%
A9275
 
3.8%
m8770
 
3.6%
e8730
 
3.5%
R8418
 
3.4%
d7749
 
3.1%
Other values (41)102888
41.7%
Common
ValueCountFrequency (%)
7676
99.2%
.56
 
0.7%
-3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII254233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a41747
 
16.4%
r16004
 
6.3%
h15811
 
6.2%
n14525
 
5.7%
i12581
 
4.9%
A9275
 
3.6%
m8770
 
3.4%
e8730
 
3.4%
R8418
 
3.3%
d7749
 
3.0%
Other values (44)110623
43.5%

FatherMiddleName
Categorical

HIGH CARDINALITY
MISSING

Distinct1477
Distinct (%)5.1%
Missing13539
Missing (%)31.7%
Memory size334.2 KiB
Prasad
4495 
Bahadur
4208 
Kumar
2793 
Raj
 
1448
BAHADUR
 
991
Other values (1472)
15283 

Length

Max length20
Median length6
Mean length5.597200356
Min length1

Characters and Unicode

Total characters163539
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique932 ?
Unique (%)3.2%

Sample

1st rowBhakta
2nd rowPrasad
3rd rowBahadur
4th rowprasad
5th rowKumar

Common Values

ValueCountFrequency (%)
Prasad4495
 
10.5%
Bahadur4208
 
9.8%
Kumar2793
 
6.5%
Raj1448
 
3.4%
BAHADUR991
 
2.3%
PRASAD878
 
2.1%
Narayan646
 
1.5%
Lal638
 
1.5%
KUMAR627
 
1.5%
Ram617
 
1.4%
Other values (1467)11877
27.8%
(Missing)13539
31.7%

Length

2021-05-15T10:16:17.737277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prasad6405
21.8%
bahadur6172
21.0%
kumar4213
14.3%
raj1927
 
6.6%
lal1004
 
3.4%
narayan914
 
3.1%
ram862
 
2.9%
nath610
 
2.1%
singh497
 
1.7%
krishna451
 
1.5%
Other values (844)6364
21.6%

Most occurring characters

ValueCountFrequency (%)
a37761
23.1%
r17758
10.9%
d11987
 
7.3%
u9508
 
5.8%
h8934
 
5.5%
A7121
 
4.4%
s6901
 
4.2%
B6573
 
4.0%
P6496
 
4.0%
R6223
 
3.8%
Other values (43)44277
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter113334
69.3%
Uppercase Letter47768
29.2%
Space Separator2323
 
1.4%
Other Punctuation113
 
0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A7121
14.9%
B6573
13.8%
P6496
13.6%
R6223
13.0%
K4853
10.2%
D2973
6.2%
S2443
 
5.1%
N2188
 
4.6%
H1948
 
4.1%
U1888
 
4.0%
Other values (15)5062
10.6%
Lowercase Letter
ValueCountFrequency (%)
a37761
33.3%
r17758
15.7%
d11987
 
10.6%
u9508
 
8.4%
h8934
 
7.9%
s6901
 
6.1%
m4662
 
4.1%
n3502
 
3.1%
j1896
 
1.7%
i1895
 
1.7%
Other values (15)8530
 
7.5%
Space Separator
ValueCountFrequency (%)
2323
100.0%
Other Punctuation
ValueCountFrequency (%)
.113
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin161102
98.5%
Common2437
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a37761
23.4%
r17758
11.0%
d11987
 
7.4%
u9508
 
5.9%
h8934
 
5.5%
A7121
 
4.4%
s6901
 
4.3%
B6573
 
4.1%
P6496
 
4.0%
R6223
 
3.9%
Other values (40)41840
26.0%
Common
ValueCountFrequency (%)
2323
95.3%
.113
 
4.6%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII163539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a37761
23.1%
r17758
10.9%
d11987
 
7.3%
u9508
 
5.8%
h8934
 
5.5%
A7121
 
4.4%
s6901
 
4.2%
B6573
 
4.0%
P6496
 
4.0%
R6223
 
3.8%
Other values (43)44277
27.1%

FatherLastName
Categorical

HIGH CARDINALITY

Distinct3633
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Shrestha
 
1779
Yadav
 
1229
Sah
 
799
Adhikari
 
792
Thapa
 
702
Other values (3628)
37456 

Length

Max length24
Median length6
Mean length6.230184531
Min length1

Characters and Unicode

Total characters266384
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1927 ?
Unique (%)4.5%

Sample

1st rowAdhikari
2nd rowYonjan
3rd rowRimal
4th rowKhadka
5th rownepal

Common Values

ValueCountFrequency (%)
Shrestha1779
 
4.2%
Yadav1229
 
2.9%
Sah799
 
1.9%
Adhikari792
 
1.9%
Thapa702
 
1.6%
Chaudhary698
 
1.6%
Sharma522
 
1.2%
Karki489
 
1.1%
Poudel463
 
1.1%
Bhattarai463
 
1.1%
Other values (3623)34821
81.4%

Length

2021-05-15T10:16:18.027349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shrestha2336
 
5.4%
yadav1728
 
4.0%
sah1255
 
2.9%
adhikari1013
 
2.3%
thapa1009
 
2.3%
chaudhary1001
 
2.3%
sharma706
 
1.6%
karki646
 
1.5%
poudel621
 
1.4%
bhattarai588
 
1.4%
Other values (1893)32538
74.9%

Most occurring characters

ValueCountFrequency (%)
a49975
18.8%
h23336
 
8.8%
r14697
 
5.5%
i14591
 
5.5%
A11392
 
4.3%
t9051
 
3.4%
e9026
 
3.4%
d8572
 
3.2%
l8184
 
3.1%
n8087
 
3.0%
Other values (46)109473
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter190028
71.3%
Uppercase Letter74551
 
28.0%
Space Separator1299
 
0.5%
Other Punctuation503
 
0.2%
Dash Punctuation3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A11392
15.3%
S7570
 
10.2%
K5250
 
7.0%
R4701
 
6.3%
H4403
 
5.9%
T4310
 
5.8%
B4307
 
5.8%
P4291
 
5.8%
D3796
 
5.1%
M3281
 
4.4%
Other values (16)21250
28.5%
Lowercase Letter
ValueCountFrequency (%)
a49975
26.3%
h23336
12.3%
r14697
 
7.7%
i14591
 
7.7%
t9051
 
4.8%
e9026
 
4.7%
d8572
 
4.5%
l8184
 
4.3%
n8087
 
4.3%
u7848
 
4.1%
Other values (16)36661
19.3%
Other Punctuation
ValueCountFrequency (%)
.501
99.6%
'2
 
0.4%
Space Separator
ValueCountFrequency (%)
1299
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin264579
99.3%
Common1805
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a49975
18.9%
h23336
 
8.8%
r14697
 
5.6%
i14591
 
5.5%
A11392
 
4.3%
t9051
 
3.4%
e9026
 
3.4%
d8572
 
3.2%
l8184
 
3.1%
n8087
 
3.1%
Other values (42)107668
40.7%
Common
ValueCountFrequency (%)
1299
72.0%
.501
 
27.8%
-3
 
0.2%
'2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII266384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a49975
18.8%
h23336
 
8.8%
r14697
 
5.5%
i14591
 
5.5%
A11392
 
4.3%
t9051
 
3.4%
e9026
 
3.4%
d8572
 
3.2%
l8184
 
3.1%
n8087
 
3.0%
Other values (46)109473
41.1%

MotherFirstName
Categorical

HIGH CARDINALITY

Distinct8196
Distinct (%)19.2%
Missing1
Missing (%)< 0.1%
Memory size334.2 KiB
Laxmi
 
797
Sita
 
741
Kamala
 
572
Gita
 
520
Mina
 
396
Other values (8191)
39730 

Length

Max length19
Median length6
Mean length5.833684161
Min length1

Characters and Unicode

Total characters249425
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5318 ?
Unique (%)12.4%

Sample

1st rowSubhadra
2nd rowLalita
3rd rowSita
4th rowSirjana
5th rowmenuka

Common Values

ValueCountFrequency (%)
Laxmi797
 
1.9%
Sita741
 
1.7%
Kamala572
 
1.3%
Gita520
 
1.2%
Mina396
 
0.9%
Nirmala373
 
0.9%
Sunita373
 
0.9%
Manju357
 
0.8%
Rita316
 
0.7%
Anita316
 
0.7%
Other values (8186)37995
88.9%

Length

2021-05-15T10:16:18.261496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
laxmi1111
 
2.6%
sita1085
 
2.5%
kamala763
 
1.8%
gita760
 
1.8%
sunita556
 
1.3%
mina554
 
1.3%
nirmala526
 
1.2%
rita477
 
1.1%
manju477
 
1.1%
anita453
 
1.1%
Other values (5341)36149
84.2%

Most occurring characters

ValueCountFrequency (%)
a51747
20.7%
i21977
 
8.8%
n13278
 
5.3%
A11566
 
4.6%
r10370
 
4.2%
t10018
 
4.0%
h9703
 
3.9%
S9557
 
3.8%
m8920
 
3.6%
u8391
 
3.4%
Other values (43)93898
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter173375
69.5%
Uppercase Letter72376
29.0%
Space Separator3648
 
1.5%
Other Punctuation20
 
< 0.1%
Dash Punctuation6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A11566
16.0%
S9557
13.2%
R5586
 
7.7%
I4957
 
6.8%
M4893
 
6.8%
N3989
 
5.5%
B3945
 
5.5%
K3637
 
5.0%
L3084
 
4.3%
T3031
 
4.2%
Other values (15)18131
25.1%
Lowercase Letter
ValueCountFrequency (%)
a51747
29.8%
i21977
12.7%
n13278
 
7.7%
r10370
 
6.0%
t10018
 
5.8%
h9703
 
5.6%
m8920
 
5.1%
u8391
 
4.8%
l6861
 
4.0%
s4875
 
2.8%
Other values (15)27235
15.7%
Space Separator
ValueCountFrequency (%)
3648
100.0%
Other Punctuation
ValueCountFrequency (%)
.20
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin245751
98.5%
Common3674
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a51747
21.1%
i21977
 
8.9%
n13278
 
5.4%
A11566
 
4.7%
r10370
 
4.2%
t10018
 
4.1%
h9703
 
3.9%
S9557
 
3.9%
m8920
 
3.6%
u8391
 
3.4%
Other values (40)90224
36.7%
Common
ValueCountFrequency (%)
3648
99.3%
.20
 
0.5%
-6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII249425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a51747
20.7%
i21977
 
8.8%
n13278
 
5.3%
A11566
 
4.6%
r10370
 
4.2%
t10018
 
4.0%
h9703
 
3.9%
S9557
 
3.8%
m8920
 
3.6%
u8391
 
3.4%
Other values (43)93898
37.6%

MotherMiddleName
Categorical

HIGH CARDINALITY
MISSING

Distinct993
Distinct (%)6.0%
Missing26118
Missing (%)61.1%
Memory size334.2 KiB
Devi
4823 
Kumari
3473 
DEVI
1159 
Maya
1151 
KUMARI
751 
Other values (988)
5282 

Length

Max length17
Median length4
Mean length4.949095499
Min length1

Characters and Unicode

Total characters82348
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique650 ?
Unique (%)3.9%

Sample

1st rowKumari
2nd rowAdhikari
3rd rowKumari
4th rowDevi
5th rowMaya

Common Values

ValueCountFrequency (%)
Devi4823
 
11.3%
Kumari3473
 
8.1%
DEVI1159
 
2.7%
Maya1151
 
2.7%
KUMARI751
 
1.8%
devi657
 
1.5%
kumari476
 
1.1%
Devi 357
 
0.8%
Kumari 280
 
0.7%
MAYA269
 
0.6%
Other values (983)3243
 
7.6%
(Missing)26118
61.1%

Length

2021-05-15T10:16:18.503911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
devi7177
42.8%
kumari5116
30.5%
maya1676
 
10.0%
laxmi351
 
2.1%
kala273
 
1.6%
sharma114
 
0.7%
thapa104
 
0.6%
maiya65
 
0.4%
keshari60
 
0.4%
singh42
 
0.3%
Other values (601)1809
 
10.8%

Most occurring characters

ValueCountFrequency (%)
i11540
14.0%
a10265
12.5%
D6620
 
8.0%
e6392
 
7.8%
v6035
 
7.3%
K5125
 
6.2%
m5102
 
6.2%
r5032
 
6.1%
u4641
 
5.6%
M2519
 
3.1%
Other values (42)19077
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter55976
68.0%
Uppercase Letter25137
30.5%
Space Separator1216
 
1.5%
Other Punctuation12
 
< 0.1%
Dash Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D6620
26.3%
K5125
20.4%
M2519
 
10.0%
I2193
 
8.7%
A1907
 
7.6%
E1273
 
5.1%
V1220
 
4.9%
R1012
 
4.0%
U872
 
3.5%
L482
 
1.9%
Other values (15)1914
 
7.6%
Lowercase Letter
ValueCountFrequency (%)
i11540
20.6%
a10265
18.3%
e6392
11.4%
v6035
10.8%
m5102
9.1%
r5032
9.0%
u4641
8.3%
y1621
 
2.9%
h970
 
1.7%
d958
 
1.7%
Other values (14)3420
 
6.1%
Space Separator
ValueCountFrequency (%)
1216
100.0%
Other Punctuation
ValueCountFrequency (%)
.12
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin81113
98.5%
Common1235
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i11540
14.2%
a10265
12.7%
D6620
 
8.2%
e6392
 
7.9%
v6035
 
7.4%
K5125
 
6.3%
m5102
 
6.3%
r5032
 
6.2%
u4641
 
5.7%
M2519
 
3.1%
Other values (39)17842
22.0%
Common
ValueCountFrequency (%)
1216
98.5%
.12
 
1.0%
-7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII82348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i11540
14.0%
a10265
12.5%
D6620
 
8.0%
e6392
 
7.8%
v6035
 
7.3%
K5125
 
6.2%
m5102
 
6.2%
r5032
 
6.1%
u4641
 
5.6%
M2519
 
3.1%
Other values (42)19077
23.2%

MotherLastName
Categorical

HIGH CARDINALITY

Distinct3801
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Shrestha
 
1786
Devi
 
1172
Yadav
 
1082
Adhikari
 
735
Thapa
 
711
Other values (3796)
37271 

Length

Max length24
Median length6
Mean length6.233084641
Min length1

Characters and Unicode

Total characters266508
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2160 ?
Unique (%)5.1%

Sample

1st rowWagle
2nd rowLama
3rd rowRimal
4th rowKhadka
5th rownepal

Common Values

ValueCountFrequency (%)
Shrestha1786
 
4.2%
Devi1172
 
2.7%
Yadav1082
 
2.5%
Adhikari735
 
1.7%
Thapa711
 
1.7%
Sah670
 
1.6%
Chaudhary640
 
1.5%
Sharma636
 
1.5%
Karki478
 
1.1%
Poudel445
 
1.0%
Other values (3791)34402
80.5%

Length

2021-05-15T10:16:18.746153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shrestha2366
 
5.4%
devi1711
 
3.9%
yadav1510
 
3.5%
thapa1031
 
2.4%
sah1019
 
2.3%
adhikari1002
 
2.3%
chaudhary912
 
2.1%
sharma891
 
2.0%
karki660
 
1.5%
poudel642
 
1.5%
Other values (1836)31869
73.1%

Most occurring characters

ValueCountFrequency (%)
a48527
18.2%
h22850
 
8.6%
i15709
 
5.9%
r14445
 
5.4%
A10785
 
4.0%
e10205
 
3.8%
t9001
 
3.4%
n8243
 
3.1%
d8216
 
3.1%
l7971
 
3.0%
Other values (45)110556
41.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter189983
71.3%
Uppercase Letter74491
 
28.0%
Space Separator1510
 
0.6%
Other Punctuation518
 
0.2%
Dash Punctuation6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A10785
14.5%
S7400
 
9.9%
K5554
 
7.5%
D5181
 
7.0%
R4419
 
5.9%
B4336
 
5.8%
H4200
 
5.6%
T4102
 
5.5%
P4063
 
5.5%
M2955
 
4.0%
Other values (16)21496
28.9%
Lowercase Letter
ValueCountFrequency (%)
a48527
25.5%
h22850
12.0%
i15709
 
8.3%
r14445
 
7.6%
e10205
 
5.4%
t9001
 
4.7%
n8243
 
4.3%
d8216
 
4.3%
l7971
 
4.2%
u7723
 
4.1%
Other values (16)37093
19.5%
Other Punctuation
ValueCountFrequency (%)
.518
100.0%
Space Separator
ValueCountFrequency (%)
1510
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin264474
99.2%
Common2034
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a48527
18.3%
h22850
 
8.6%
i15709
 
5.9%
r14445
 
5.5%
A10785
 
4.1%
e10205
 
3.9%
t9001
 
3.4%
n8243
 
3.1%
d8216
 
3.1%
l7971
 
3.0%
Other values (42)108522
41.0%
Common
ValueCountFrequency (%)
1510
74.2%
.518
 
25.5%
-6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII266508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a48527
18.2%
h22850
 
8.6%
i15709
 
5.9%
r14445
 
5.4%
A10785
 
4.0%
e10205
 
3.8%
t9001
 
3.4%
n8243
 
3.1%
d8216
 
3.1%
l7971
 
3.0%
Other values (45)110556
41.5%

DistrictCode
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75
Distinct (%)0.2%
Missing41
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean30.62547991
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:18.857192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q117
median27
Q340
95-th percentile69
Maximum75
Range74
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.05690955
Coefficient of variation (CV)0.5896041335
Kurtosis-0.3153163698
Mean30.62547991
Median Absolute Deviation (MAD)12
Skewness0.5513355303
Sum1308198
Variance326.0519824
MonotonicityNot monotonic
2021-05-15T10:16:18.998491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273439
 
8.0%
151969
 
4.6%
51842
 
4.3%
171786
 
4.2%
401565
 
3.7%
61539
 
3.6%
261482
 
3.5%
351349
 
3.2%
251348
 
3.2%
41340
 
3.1%
Other values (65)25057
58.6%
ValueCountFrequency (%)
1103
 
0.2%
2139
 
0.3%
3244
 
0.6%
41340
3.1%
51842
4.3%
61539
3.6%
7136
 
0.3%
8104
 
0.2%
9149
 
0.3%
10137
 
0.3%
ValueCountFrequency (%)
75190
 
0.4%
74236
 
0.6%
73182
 
0.4%
72697
1.6%
71652
1.5%
70115
 
0.3%
69234
 
0.5%
68187
 
0.4%
6798
 
0.2%
6662
 
0.1%

DistrictName
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct75
Distinct (%)0.2%
Missing41
Missing (%)0.1%
Memory size334.2 KiB
Kathmandu
3439 
Saptari
 
1969
Morang
 
1842
Dhanusha
 
1786
Kaski
 
1565
Other values (70)
32115 

Length

Max length14
Median length7
Mean length7.548295721
Min length4

Characters and Unicode

Total characters322433
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChitawan
2nd rowSiraha
3rd rowNuwakot
4th rowDolakha
5th rowPanchthar

Common Values

ValueCountFrequency (%)
Kathmandu3439
 
8.0%
Saptari1969
 
4.6%
Morang1842
 
4.3%
Dhanusha1786
 
4.2%
Kaski1565
 
3.7%
Sunsari1539
 
3.6%
Bhaktapur1482
 
3.5%
Chitawan1349
 
3.2%
Lalitpur1348
 
3.2%
Jhapa1340
 
3.1%
Other values (65)25057
58.6%

Length

2021-05-15T10:16:19.248310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kathmandu3439
 
8.1%
saptari1969
 
4.6%
morang1842
 
4.3%
dhanusha1786
 
4.2%
kaski1565
 
3.7%
sunsari1539
 
3.6%
bhaktapur1482
 
3.5%
chitawan1349
 
3.2%
lalitpur1348
 
3.2%
jhapa1340
 
3.1%
Other values (65)25057
58.7%

Most occurring characters

ValueCountFrequency (%)
a72086
22.4%
h27742
 
8.6%
n21670
 
6.7%
u19729
 
6.1%
r19531
 
6.1%
i19169
 
5.9%
t16802
 
5.2%
p12773
 
4.0%
l10387
 
3.2%
k8877
 
2.8%
Other values (30)93667
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter279717
86.8%
Uppercase Letter42716
 
13.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a72086
25.8%
h27742
 
9.9%
n21670
 
7.7%
u19729
 
7.1%
r19531
 
7.0%
i19169
 
6.9%
t16802
 
6.0%
p12773
 
4.6%
l10387
 
3.7%
k8877
 
3.2%
Other values (12)50951
18.2%
Uppercase Letter
ValueCountFrequency (%)
S8209
19.2%
K7935
18.6%
D4395
10.3%
M3911
9.2%
B3901
9.1%
R2638
 
6.2%
P1880
 
4.4%
L1752
 
4.1%
J1529
 
3.6%
N1409
 
3.3%
Other values (8)5157
12.1%

Most occurring scripts

ValueCountFrequency (%)
Latin322433
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a72086
22.4%
h27742
 
8.6%
n21670
 
6.7%
u19729
 
6.1%
r19531
 
6.1%
i19169
 
5.9%
t16802
 
5.2%
p12773
 
4.0%
l10387
 
3.2%
k8877
 
2.8%
Other values (30)93667
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII322433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a72086
22.4%
h27742
 
8.6%
n21670
 
6.7%
u19729
 
6.1%
r19531
 
6.1%
i19169
 
5.9%
t16802
 
5.2%
p12773
 
4.0%
l10387
 
3.2%
k8877
 
2.8%
Other values (30)93667
29.1%

NationalityName
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Nepali
42716 
SAARC
 
39
Others
 
2

Length

Max length6
Median length6
Mean length5.999087869
Min length5

Characters and Unicode

Total characters256503
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNepali
2nd rowNepali
3rd rowNepali
4th rowNepali
5th rowNepali

Common Values

ValueCountFrequency (%)
Nepali42716
99.9%
SAARC39
 
0.1%
Others2
 
< 0.1%

Length

2021-05-15T10:16:19.421831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:19.482400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
nepali42716
99.9%
saarc39
 
0.1%
others2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e42718
16.7%
N42716
16.7%
p42716
16.7%
a42716
16.7%
l42716
16.7%
i42716
16.7%
A78
 
< 0.1%
S39
 
< 0.1%
R39
 
< 0.1%
C39
 
< 0.1%
Other values (5)10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter213590
83.3%
Uppercase Letter42913
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e42718
20.0%
p42716
20.0%
a42716
20.0%
l42716
20.0%
i42716
20.0%
t2
 
< 0.1%
h2
 
< 0.1%
r2
 
< 0.1%
s2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N42716
99.5%
A78
 
0.2%
S39
 
0.1%
R39
 
0.1%
C39
 
0.1%
O2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin256503
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e42718
16.7%
N42716
16.7%
p42716
16.7%
a42716
16.7%
l42716
16.7%
i42716
16.7%
A78
 
< 0.1%
S39
 
< 0.1%
R39
 
< 0.1%
C39
 
< 0.1%
Other values (5)10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII256503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e42718
16.7%
N42716
16.7%
p42716
16.7%
a42716
16.7%
l42716
16.7%
i42716
16.7%
A78
 
< 0.1%
S39
 
< 0.1%
R39
 
< 0.1%
C39
 
< 0.1%
Other values (5)10
 
< 0.1%

IdentificationTypeName
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Citizenship
34699 
Last Exam Admit Card
7412 
Driving License
 
365
Passport
 
261
Voter's Card
 
20

Length

Max length20
Median length11
Mean length12.57646701
Min length8

Characters and Unicode

Total characters537732
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCitizenship
2nd rowCitizenship
3rd rowCitizenship
4th rowCitizenship
5th rowCitizenship

Common Values

ValueCountFrequency (%)
Citizenship34699
81.2%
Last Exam Admit Card7412
 
17.3%
Driving License365
 
0.9%
Passport261
 
0.6%
Voter's Card20
 
< 0.1%

Length

2021-05-15T10:16:19.643802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:19.714501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
citizenship34699
53.1%
card7432
 
11.4%
admit7412
 
11.3%
exam7412
 
11.3%
last7412
 
11.3%
driving365
 
0.6%
license365
 
0.6%
passport261
 
0.4%
voter's20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i112604
20.9%
t49804
9.3%
s43018
 
8.0%
C42131
 
7.8%
e35449
 
6.6%
n35429
 
6.6%
p34960
 
6.5%
z34699
 
6.5%
h34699
 
6.5%
22621
 
4.2%
Other values (16)92318
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter449713
83.6%
Uppercase Letter65378
 
12.2%
Space Separator22621
 
4.2%
Other Punctuation20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i112604
25.0%
t49804
11.1%
s43018
 
9.6%
e35449
 
7.9%
n35429
 
7.9%
p34960
 
7.8%
z34699
 
7.7%
h34699
 
7.7%
a22517
 
5.0%
d14844
 
3.3%
Other values (7)31690
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
C42131
64.4%
L7777
 
11.9%
E7412
 
11.3%
A7412
 
11.3%
D365
 
0.6%
P261
 
0.4%
V20
 
< 0.1%
Space Separator
ValueCountFrequency (%)
22621
100.0%
Other Punctuation
ValueCountFrequency (%)
'20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin515091
95.8%
Common22641
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i112604
21.9%
t49804
9.7%
s43018
 
8.4%
C42131
 
8.2%
e35449
 
6.9%
n35429
 
6.9%
p34960
 
6.8%
z34699
 
6.7%
h34699
 
6.7%
a22517
 
4.4%
Other values (14)69781
13.5%
Common
ValueCountFrequency (%)
22621
99.9%
'20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII537732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i112604
20.9%
t49804
9.3%
s43018
 
8.0%
C42131
 
7.8%
e35449
 
6.6%
n35429
 
6.6%
p34960
 
6.5%
z34699
 
6.5%
h34699
 
6.5%
22621
 
4.2%
Other values (16)92318
17.2%

FullName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct36272
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
Sujan Shrestha
 
17
Bibek Shrestha
 
16
Santosh Kumar Yadav
 
14
Rahul Kumar Yadav
 
14
Sagar Adhikari
 
13
Other values (36267)
42683 

Length

Max length50
Median length14
Mean length14.55911313
Min length5

Characters and Unicode

Total characters622504
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32233 ?
Unique (%)75.4%

Sample

1st rowAashutosh Adhikari
2nd rowBikal Yonjan
3rd rowShramik Rimal
4th rowSHISHIR KHADKA
5th rowdurga raj nepal

Common Values

ValueCountFrequency (%)
Sujan Shrestha17
 
< 0.1%
Bibek Shrestha16
 
< 0.1%
Santosh Kumar Yadav14
 
< 0.1%
Rahul Kumar Yadav14
 
< 0.1%
Sagar Adhikari13
 
< 0.1%
Aayush Shrestha13
 
< 0.1%
Anish Shrestha13
 
< 0.1%
Aashish Shrestha12
 
< 0.1%
Sabin Shrestha12
 
< 0.1%
Ajay Kumar Yadav12
 
< 0.1%
Other values (36262)42621
99.7%

Length

2021-05-15T10:16:19.936505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kumar4053
 
4.3%
shrestha2361
 
2.5%
yadav1866
 
2.0%
sah1240
 
1.3%
adhikari1091
 
1.1%
thapa1091
 
1.1%
chaudhary1037
 
1.1%
raj906
 
1.0%
poudel708
 
0.7%
singh676
 
0.7%
Other values (8789)80079
84.2%

Most occurring characters

ValueCountFrequency (%)
a99145
15.9%
56400
 
9.1%
h43715
 
7.0%
i35894
 
5.8%
r32140
 
5.2%
n25799
 
4.1%
A23014
 
3.7%
s21113
 
3.4%
u20938
 
3.4%
S20654
 
3.3%
Other values (47)243692
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter416893
67.0%
Uppercase Letter148487
 
23.9%
Space Separator56400
 
9.1%
Other Punctuation704
 
0.1%
Dash Punctuation8
 
< 0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A23014
15.5%
S20654
13.9%
K11804
 
7.9%
R11623
 
7.8%
B9293
 
6.3%
P9196
 
6.2%
N7046
 
4.7%
M6791
 
4.6%
H6672
 
4.5%
D6532
 
4.4%
Other values (16)35862
24.2%
Lowercase Letter
ValueCountFrequency (%)
a99145
23.8%
h43715
10.5%
i35894
 
8.6%
r32140
 
7.7%
n25799
 
6.2%
s21113
 
5.1%
u20938
 
5.0%
e18379
 
4.4%
t15787
 
3.8%
d14569
 
3.5%
Other values (16)89414
21.4%
Space Separator
ValueCountFrequency (%)
56400
100.0%
Other Punctuation
ValueCountFrequency (%)
.704
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin565380
90.8%
Common57124
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a99145
17.5%
h43715
 
7.7%
i35894
 
6.3%
r32140
 
5.7%
n25799
 
4.6%
A23014
 
4.1%
s21113
 
3.7%
u20938
 
3.7%
S20654
 
3.7%
e18379
 
3.3%
Other values (42)224589
39.7%
Common
ValueCountFrequency (%)
56400
98.7%
.704
 
1.2%
-8
 
< 0.1%
(6
 
< 0.1%
)6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII622504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a99145
15.9%
56400
 
9.1%
h43715
 
7.0%
i35894
 
5.8%
r32140
 
5.2%
n25799
 
4.1%
A23014
 
3.7%
s21113
 
3.4%
u20938
 
3.4%
S20654
 
3.3%
Other values (47)243692
39.1%

EntranceScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1103
Distinct (%)5.5%
Missing22859
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean61.43524435
Minimum35
Maximum131.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:20.047470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile38.714
Q149.8
median57.714
Q370.7
95-th percentile94.9
Maximum131.2
Range96.2
Interquartile range (IQR)20.9

Descriptive statistics

Standard deviation16.81597003
Coefficient of variation (CV)0.2737186155
Kurtosis0.7600932611
Mean61.43524435
Median Absolute Deviation (MAD)9.571
Skewness0.9499870685
Sum1222438.492
Variance282.776848
MonotonicityNot monotonic
2021-05-15T10:16:20.166523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.7413
 
1.0%
49.8403
 
0.9%
50.9396
 
0.9%
54.2377
 
0.9%
53.1374
 
0.9%
52373
 
0.9%
55.3348
 
0.8%
58.6323
 
0.8%
59.7321
 
0.8%
56.4315
 
0.7%
Other values (1093)16255
38.0%
(Missing)22859
53.5%
ValueCountFrequency (%)
359
 
< 0.1%
35.0716
 
< 0.1%
35.1432
 
< 0.1%
35.2149
 
< 0.1%
35.2863
 
< 0.1%
35.3573
 
< 0.1%
35.4293
 
< 0.1%
35.51
 
< 0.1%
35.571178
0.4%
35.6438
 
< 0.1%
ValueCountFrequency (%)
131.23
 
< 0.1%
1292
 
< 0.1%
127.91
 
< 0.1%
126.84
 
< 0.1%
125.75
 
< 0.1%
124.62
 
< 0.1%
123.514
< 0.1%
122.72
 
< 0.1%
122.61
 
< 0.1%
122.46
< 0.1%

EntranceRank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6999
Distinct (%)35.2%
Missing22859
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean3325.762589
Minimum1
Maximum6999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:20.297755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile332
Q11659
median3317
Q34975
95-th percentile6365
Maximum6999
Range6998
Interquartile range (IQR)3316

Descriptive statistics

Standard deviation1929.885865
Coefficient of variation (CV)0.5802837135
Kurtosis-1.165900827
Mean3325.762589
Median Absolute Deviation (MAD)1658
Skewness0.02600939138
Sum66176024
Variance3724459.454
MonotonicityNot monotonic
2021-05-15T10:16:20.410872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35843
 
< 0.1%
31023
 
< 0.1%
19103
 
< 0.1%
5733
 
< 0.1%
24963
 
< 0.1%
55613
 
< 0.1%
48663
 
< 0.1%
37333
 
< 0.1%
3433
 
< 0.1%
32453
 
< 0.1%
Other values (6989)19868
46.5%
(Missing)22859
53.5%
ValueCountFrequency (%)
13
< 0.1%
23
< 0.1%
33
< 0.1%
43
< 0.1%
53
< 0.1%
63
< 0.1%
73
< 0.1%
83
< 0.1%
93
< 0.1%
103
< 0.1%
ValueCountFrequency (%)
69991
< 0.1%
69981
< 0.1%
69971
< 0.1%
69961
< 0.1%
69951
< 0.1%
69941
< 0.1%
69931
< 0.1%
69921
< 0.1%
69911
< 0.1%
69901
< 0.1%

date
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.2 KiB
2077
12708 
2074
12309 
2073
10741 
2076
6999 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters171028
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2073
2nd row2073
3rd row2073
4th row2073
5th row2073

Common Values

ValueCountFrequency (%)
207712708
29.7%
207412309
28.8%
207310741
25.1%
20766999
16.4%

Length

2021-05-15T10:16:20.612857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:20.673487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
207712708
29.7%
207412309
28.8%
207310741
25.1%
20766999
16.4%

Most occurring characters

ValueCountFrequency (%)
755465
32.4%
242757
25.0%
042757
25.0%
412309
 
7.2%
310741
 
6.3%
66999
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number171028
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
755465
32.4%
242757
25.0%
042757
25.0%
412309
 
7.2%
310741
 
6.3%
66999
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common171028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
755465
32.4%
242757
25.0%
042757
25.0%
412309
 
7.2%
310741
 
6.3%
66999
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII171028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
755465
32.4%
242757
25.0%
042757
25.0%
412309
 
7.2%
310741
 
6.3%
66999
 
4.1%

StudentID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32016
Distinct (%)100.0%
Missing10741
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean33386.17504
Minimum1
Maximum70299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:20.764465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1601.75
Q111949.75
median38199.5
Q346203.25
95-th percentile66251.5
Maximum70299
Range70298
Interquartile range (IQR)34253.5

Descriptive statistics

Standard deviation21761.56304
Coefficient of variation (CV)0.6518136029
Kurtosis-1.278212106
Mean33386.17504
Median Absolute Deviation (MAD)21338
Skewness-0.02981892068
Sum1068891780
Variance473565625.8
MonotonicityNot monotonic
2021-05-15T10:16:20.885746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83451
 
< 0.1%
43811
 
< 0.1%
193511
 
< 0.1%
351891
 
< 0.1%
200391
 
< 0.1%
197241
 
< 0.1%
435251
 
< 0.1%
194521
 
< 0.1%
692811
 
< 0.1%
22311
 
< 0.1%
Other values (32006)32006
74.9%
(Missing)10741
 
25.1%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
702991
< 0.1%
702931
< 0.1%
702921
< 0.1%
702861
< 0.1%
702791
< 0.1%
702781
< 0.1%
702691
< 0.1%
702681
< 0.1%
702631
< 0.1%
702621
< 0.1%

VoucherNo
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10741
Missing (%)25.1%
Memory size334.2 KiB

StudentCode
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct31342
Distinct (%)97.9%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
2021-20183
 
7
2021-20170
 
6
2021-20178
 
5
2021-4970
 
5
2021-20218
 
4
Other values (31337)
31989 

Length

Max length10
Median length10
Mean length9.731509245
Min length6

Characters and Unicode

Total characters311564
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30721 ?
Unique (%)96.0%

Sample

1st row2017-1
2nd row2017-34901
3rd row2017-34902
4th row2017-34903
5th row2017-34904

Common Values

ValueCountFrequency (%)
2021-201837
 
< 0.1%
2021-201706
 
< 0.1%
2021-201785
 
< 0.1%
2021-49705
 
< 0.1%
2021-202184
 
< 0.1%
2021-201044
 
< 0.1%
2021-201014
 
< 0.1%
2021-201434
 
< 0.1%
2021-202304
 
< 0.1%
2017-412293
 
< 0.1%
Other values (31332)31970
74.8%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:21.128137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-201837
 
< 0.1%
2021-201706
 
< 0.1%
2021-201785
 
< 0.1%
2021-49705
 
< 0.1%
2021-201014
 
< 0.1%
2021-201434
 
< 0.1%
2021-202304
 
< 0.1%
2021-202184
 
< 0.1%
2021-201044
 
< 0.1%
2021-200973
 
< 0.1%
Other values (31332)31970
99.9%

Most occurring characters

ValueCountFrequency (%)
257611
18.5%
149256
15.8%
044392
14.2%
-32016
10.3%
724438
7.8%
920188
 
6.5%
419830
 
6.4%
618058
 
5.8%
318030
 
5.8%
514310
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number279548
89.7%
Dash Punctuation32016
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
257611
20.6%
149256
17.6%
044392
15.9%
724438
8.7%
920188
 
7.2%
419830
 
7.1%
618058
 
6.5%
318030
 
6.4%
514310
 
5.1%
813435
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common311564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
257611
18.5%
149256
15.8%
044392
14.2%
-32016
10.3%
724438
7.8%
920188
 
6.5%
419830
 
6.4%
618058
 
5.8%
318030
 
5.8%
514310
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII311564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
257611
18.5%
149256
15.8%
044392
14.2%
-32016
10.3%
724438
7.8%
920188
 
6.5%
419830
 
6.4%
618058
 
5.8%
318030
 
5.8%
514310
 
4.6%

ContactNo
Real number (ℝ≥0)

MISSING
SKEWED

Distinct31437
Distinct (%)98.2%
Missing10741
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean2.919398868 × 1010
Minimum1146136
Maximum9.779880255 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:21.237233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1146136
5-th percentile9804055726
Q19814798389
median9840887842
Q39861019832
95-th percentile9868272674
Maximum9.779880255 × 1012
Range9.779879108 × 1012
Interquartile range (IQR)46221443.25

Descriptive statistics

Standard deviation4.330771431 × 1011
Coefficient of variation (CV)14.83446294
Kurtosis502.7580922
Mean2.919398868 × 1010
Median Absolute Deviation (MAD)22556511
Skewness22.46132012
Sum9.346747417 × 1014
Variance1.875558119 × 1023
MonotonicityNot monotonic
2021-05-15T10:16:21.360456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98468201123
 
< 0.1%
98078635473
 
< 0.1%
98192365723
 
< 0.1%
98609898053
 
< 0.1%
98512990993
 
< 0.1%
97410741183
 
< 0.1%
98438989433
 
< 0.1%
98404185633
 
< 0.1%
98624671733
 
< 0.1%
98625654073
 
< 0.1%
Other values (31427)31986
74.8%
(Missing)10741
 
25.1%
ValueCountFrequency (%)
11461361
< 0.1%
98698401
< 0.1%
116602671
< 0.1%
140341431
< 0.1%
140391111
< 0.1%
140391221
< 0.1%
141095421
< 0.1%
141143881
< 0.1%
141552691
< 0.1%
142507912
< 0.1%
ValueCountFrequency (%)
9.779880255 × 10121
< 0.1%
9.779869377 × 10121
< 0.1%
9.779868835 × 10121
< 0.1%
9.779867342 × 10121
< 0.1%
9.779867116 × 10121
< 0.1%
9.779866822 × 10121
< 0.1%
9.779865959 × 10121
< 0.1%
9.779865595 × 10121
< 0.1%
9.779864254 × 10121
< 0.1%
9.779863948 × 10121
< 0.1%

Email
Categorical

HIGH CARDINALITY
MISSING

Distinct30138
Distinct (%)94.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
bkbinod070bex@gmail.com
 
207
abc@gmail.com
 
176
gradientacademypkr@gmail.com
 
148
neonepaledu@gmail.com
 
77
Bkbinod070bex@gmail.com
 
68
Other values (30133)
31340 

Length

Max length40
Median length24
Mean length23.38546352
Min length11

Characters and Unicode

Total characters748709
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29435 ?
Unique (%)91.9%

Sample

1st rowalayniaz@gmail.com
2nd rowrupacepakhrin@gmail.com
3rd rowpaudelrajbibek@gmail.com
4th rowluckshrestha@gmail.com
5th rowaaceesh.bhandary@gmail.com

Common Values

ValueCountFrequency (%)
bkbinod070bex@gmail.com207
 
0.5%
abc@gmail.com176
 
0.4%
gradientacademypkr@gmail.com148
 
0.3%
neonepaledu@gmail.com77
 
0.2%
Bkbinod070bex@gmail.com68
 
0.2%
beacareer@gmail.com65
 
0.2%
BKbinod070bex@gmail.com61
 
0.1%
kcpawan07@gmail.com44
 
0.1%
ABC@GMAIL.COM40
 
0.1%
bik3sh@yahoo.com38
 
0.1%
Other values (30128)31092
72.7%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:21.623002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bkbinod070bex@gmail.com337
 
1.1%
abc@gmail.com219
 
0.7%
gradientacademypkr@gmail.com148
 
0.5%
neonepaledu@gmail.com77
 
0.2%
beacareer@gmail.com65
 
0.2%
kcpawan07@gmail.com44
 
0.1%
bik3sh@yahoo.com38
 
0.1%
brijkishorprasad196@gmail.com30
 
0.1%
prakash.singhvision@gmail.com29
 
0.1%
barunjha2007@gmail.com24
 
0.1%
Other values (30043)31005
96.8%

Most occurring characters

ValueCountFrequency (%)
a97912
 
13.1%
m71890
 
9.6%
i55720
 
7.4%
l41245
 
5.5%
o40066
 
5.4%
g35835
 
4.8%
.35827
 
4.8%
c35155
 
4.7%
@32016
 
4.3%
h28255
 
3.8%
Other values (57)274788
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter586057
78.3%
Decimal Number73433
 
9.8%
Other Punctuation67843
 
9.1%
Uppercase Letter21148
 
2.8%
Connector Punctuation211
 
< 0.1%
Dash Punctuation16
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a97912
16.7%
m71890
12.3%
i55720
9.5%
l41245
 
7.0%
o40066
 
6.8%
g35835
 
6.1%
c35155
 
6.0%
h28255
 
4.8%
s25628
 
4.4%
r23622
 
4.0%
Other values (16)130729
22.3%
Uppercase Letter
ValueCountFrequency (%)
A3302
15.6%
M2456
11.6%
I1807
 
8.5%
L1404
 
6.6%
O1395
 
6.6%
S1353
 
6.4%
C1285
 
6.1%
G1215
 
5.7%
H921
 
4.4%
R881
 
4.2%
Other values (16)5129
24.3%
Decimal Number
ValueCountFrequency (%)
111230
15.3%
010486
14.3%
29550
13.0%
36654
9.1%
76642
9.0%
96576
9.0%
56295
8.6%
45646
7.7%
85571
7.6%
64783
6.5%
Other Punctuation
ValueCountFrequency (%)
.35827
52.8%
@32016
47.2%
Connector Punctuation
ValueCountFrequency (%)
_211
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin607205
81.1%
Common141504
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a97912
16.1%
m71890
11.8%
i55720
 
9.2%
l41245
 
6.8%
o40066
 
6.6%
g35835
 
5.9%
c35155
 
5.8%
h28255
 
4.7%
s25628
 
4.2%
r23622
 
3.9%
Other values (42)151877
25.0%
Common
ValueCountFrequency (%)
.35827
25.3%
@32016
22.6%
111230
 
7.9%
010486
 
7.4%
29550
 
6.7%
36654
 
4.7%
76642
 
4.7%
96576
 
4.6%
56295
 
4.4%
45646
 
4.0%
Other values (5)10582
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII748709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a97912
 
13.1%
m71890
 
9.6%
i55720
 
7.4%
l41245
 
5.5%
o40066
 
5.4%
g35835
 
4.8%
.35827
 
4.8%
c35155
 
4.7%
@32016
 
4.3%
h28255
 
3.8%
Other values (57)274788
36.7%

NationalityID
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
31988 
2.0
 
27
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031988
74.8%
2.027
 
0.1%
3.01
 
< 0.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:21.814808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:21.875308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031988
99.9%
2.027
 
0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131988
33.3%
227
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
131988
50.0%
227
 
< 0.1%
31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131988
33.3%
227
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131988
33.3%
227
 
< 0.1%
31
 
< 0.1%

CountryID
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
31988 
2.0
 
27
148.0
 
1

Length

Max length5
Median length3
Mean length3.000062469
Min length3

Characters and Unicode

Total characters96050
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031988
74.8%
2.027
 
0.1%
148.01
 
< 0.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:22.036600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:22.108148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031988
99.9%
2.027
 
0.1%
148.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131989
33.3%
227
 
< 0.1%
41
 
< 0.1%
81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64034
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
131989
50.0%
227
 
< 0.1%
41
 
< 0.1%
81
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131989
33.3%
227
 
< 0.1%
41
 
< 0.1%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131989
33.3%
227
 
< 0.1%
41
 
< 0.1%
81
 
< 0.1%

IndianEmbassyID
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42757
Missing (%)100.0%
Memory size334.2 KiB

ExamCenterID
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
1.0
32012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96036
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:22.256455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:22.308941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64024
66.7%
Other Punctuation32012
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132012
50.0%
032012
50.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

ExamSessionID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct95
Distinct (%)0.3%
Missing10745
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean39.21432588
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:22.379602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median33
Q350
95-th percentile94
Maximum110
Range109
Interquartile range (IQR)32

Descriptive statistics

Standard deviation27.83993469
Coefficient of variation (CV)0.7099429625
Kurtosis-0.3606547489
Mean39.21432588
Median Absolute Deviation (MAD)16
Skewness0.7504893627
Sum1255329
Variance775.0619634
MonotonicityNot monotonic
2021-05-15T10:16:22.500735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24683
 
1.6%
27660
 
1.5%
25655
 
1.5%
26654
 
1.5%
28649
 
1.5%
23649
 
1.5%
1633
 
1.5%
4629
 
1.5%
3626
 
1.5%
2570
 
1.3%
Other values (85)25604
59.9%
(Missing)10745
25.1%
ValueCountFrequency (%)
1633
1.5%
2570
1.3%
3626
1.5%
4629
1.5%
5355
0.8%
6356
0.8%
7414
1.0%
8402
0.9%
9374
0.9%
10370
0.9%
ValueCountFrequency (%)
11043
 
0.1%
10955
0.1%
10886
0.2%
10787
0.2%
10691
0.2%
105104
0.2%
104104
0.2%
10379
0.2%
10295
0.2%
101115
0.3%

ZoneID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct14
Distinct (%)< 0.1%
Missing10769
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean5.318463174
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:22.601355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.803603932
Coefficient of variation (CV)0.7151697413
Kurtosis-0.7018717659
Mean5.318463174
Median Absolute Deviation (MAD)3
Skewness0.592119576
Sum170127
Variance14.46740287
MonotonicityNot monotonic
2021-05-15T10:16:22.690690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
16626
15.5%
23936
 
9.2%
83265
 
7.6%
93092
 
7.2%
63005
 
7.0%
42884
 
6.7%
32771
 
6.5%
51379
 
3.2%
121070
 
2.5%
131002
 
2.3%
Other values (4)2958
 
6.9%
(Missing)10769
25.2%
ValueCountFrequency (%)
16626
15.5%
23936
9.2%
32771
6.5%
42884
6.7%
51379
 
3.2%
63005
7.0%
7885
 
2.1%
83265
7.6%
93092
7.2%
10843
 
2.0%
ValueCountFrequency (%)
14962
 
2.2%
131002
 
2.3%
121070
 
2.5%
11268
 
0.6%
10843
 
2.0%
93092
7.2%
83265
7.6%
7885
 
2.1%
63005
7.0%
51379
3.2%

DistrictID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct75
Distinct (%)0.2%
Missing10769
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean36.86632487
Minimum4
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:22.793668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q118
median39
Q356
95-th percentile70
Maximum78
Range74
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.470467
Coefficient of variation (CV)0.609511989
Kurtosis-1.228402071
Mean36.86632487
Median Absolute Deviation (MAD)19
Skewness0.02904745296
Sum1179280
Variance504.9218872
MonotonicityNot monotonic
2021-05-15T10:16:22.914741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42599
 
6.1%
691388
 
3.2%
431340
 
3.1%
321309
 
3.1%
241209
 
2.8%
421141
 
2.7%
51099
 
2.6%
6999
 
2.3%
54996
 
2.3%
62990
 
2.3%
Other values (65)18918
44.2%
(Missing)10769
25.2%
ValueCountFrequency (%)
42599
6.1%
51099
2.6%
6999
 
2.3%
7672
 
1.6%
8368
 
0.9%
9391
 
0.9%
1056
 
0.1%
11439
 
1.0%
12267
 
0.6%
13212
 
0.5%
ValueCountFrequency (%)
78494
 
1.2%
7788
 
0.2%
76179
 
0.4%
75134
 
0.3%
7467
 
0.2%
7387
 
0.2%
72159
 
0.4%
71166
 
0.4%
70234
 
0.5%
691388
3.2%

SLCEquivalentID
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
30597 
4.0
 
906
3.0
 
506
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.030597
71.6%
4.0906
 
2.1%
3.0506
 
1.2%
2.07
 
< 0.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:23.116690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:23.177257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.030597
95.6%
4.0906
 
2.8%
3.0506
 
1.6%
2.07
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130597
31.9%
4906
 
0.9%
3506
 
0.5%
27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
130597
47.8%
4906
 
1.4%
3506
 
0.8%
27
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130597
31.9%
4906
 
0.9%
3506
 
0.5%
27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130597
31.9%
4906
 
0.9%
3506
 
0.5%
27
 
< 0.1%

SLCBoardID
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
31557 
2.0
 
459

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031557
73.8%
2.0459
 
1.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:23.328674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:23.379157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031557
98.6%
2.0459
 
1.4%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131557
32.9%
2459
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
131557
49.3%
2459
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131557
32.9%
2459
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
131557
32.9%
2459
 
0.5%

PCLEquivalentID
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
30234 
3.0
 
914
2.0
 
470
5.0
 
324
6.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.030234
70.7%
3.0914
 
2.1%
2.0470
 
1.1%
5.0324
 
0.8%
6.074
 
0.2%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:23.540745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:23.601298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.030234
94.4%
3.0914
 
2.9%
2.0470
 
1.5%
5.0324
 
1.0%
6.074
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130234
31.5%
3914
 
1.0%
2470
 
0.5%
5324
 
0.3%
674
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
130234
47.2%
3914
 
1.4%
2470
 
0.7%
5324
 
0.5%
674
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130234
31.5%
3914
 
1.0%
2470
 
0.5%
5324
 
0.3%
674
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
130234
31.5%
3914
 
1.0%
2470
 
0.5%
5324
 
0.3%
674
 
0.1%

PCLBoardID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean1.228104698
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:23.661903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9330409442
Coefficient of variation (CV)0.7597405547
Kurtosis16.32177795
Mean1.228104698
Median Absolute Deviation (MAD)0
Skewness4.161409768
Sum39319
Variance0.8705654035
MonotonicityNot monotonic
2021-05-15T10:16:23.742683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
129997
70.2%
6697
 
1.6%
4685
 
1.6%
5322
 
0.8%
3160
 
0.4%
2155
 
0.4%
(Missing)10741
 
25.1%
ValueCountFrequency (%)
129997
70.2%
2155
 
0.4%
3160
 
0.4%
4685
 
1.6%
5322
 
0.8%
6697
 
1.6%
ValueCountFrequency (%)
6697
 
1.6%
5322
 
0.8%
4685
 
1.6%
3160
 
0.4%
2155
 
0.4%
129997
70.2%

PCLResultTypeID
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
25574 
5.0
6442 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.025574
59.8%
5.06442
 
15.1%
(Missing)10741
25.1%

Length

2021-05-15T10:16:23.914478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:23.964982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.025574
79.9%
5.06442
 
20.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
125574
26.6%
56442
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
125574
39.9%
56442
 
10.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
125574
26.6%
56442
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
125574
26.6%
56442
 
6.7%

PCLLocationID
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
0.0
32016 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.032016
74.9%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:24.106396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:24.164970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.032016
100.0%

Most occurring characters

ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064032
100.0%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

EthnicGroupSpecify
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing42755
Missing (%)> 99.9%
Memory size334.2 KiB
Chhettri
Bhramin

Length

Max length8
Median length7.5
Mean length7.5
Min length7

Characters and Unicode

Total characters15
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowBhramin
2nd rowChhettri

Common Values

ValueCountFrequency (%)
Chhettri1
 
< 0.1%
Bhramin1
 
< 0.1%
(Missing)42755
> 99.9%

Length

2021-05-15T10:16:24.306365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:24.358830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bhramin1
50.0%
chhettri1
50.0%

Most occurring characters

ValueCountFrequency (%)
h3
20.0%
r2
13.3%
i2
13.3%
t2
13.3%
B1
 
6.7%
a1
 
6.7%
m1
 
6.7%
n1
 
6.7%
C1
 
6.7%
e1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13
86.7%
Uppercase Letter2
 
13.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h3
23.1%
r2
15.4%
i2
15.4%
t2
15.4%
a1
 
7.7%
m1
 
7.7%
n1
 
7.7%
e1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
B1
50.0%
C1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h3
20.0%
r2
13.3%
i2
13.3%
t2
13.3%
B1
 
6.7%
a1
 
6.7%
m1
 
6.7%
n1
 
6.7%
C1
 
6.7%
e1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h3
20.0%
r2
13.3%
i2
13.3%
t2
13.3%
B1
 
6.7%
a1
 
6.7%
m1
 
6.7%
n1
 
6.7%
C1
 
6.7%
e1
 
6.7%

SLCEquivalentSpecify
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing41761
Missing (%)97.7%
Memory size334.2 KiB

SLCBoardSpecify
Categorical

HIGH CARDINALITY
MISSING

Distinct113
Distinct (%)23.0%
Missing42266
Missing (%)98.9%
Memory size334.2 KiB
NEB
177 
CBSE
111 
SEE
22 
Private
18 
SLC
 
9
Other values (108)
154 

Length

Max length73
Median length4
Mean length6.759674134
Min length1

Characters and Unicode

Total characters3319
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)17.7%

Sample

1st rowSchool Leaving Certificate Examination
2nd rowSLC
3rd rowCBSE board
4th rowH.M.G
5th rowCBSE

Common Values

ValueCountFrequency (%)
NEB177
 
0.4%
CBSE111
 
0.3%
SEE22
 
0.1%
Private18
 
< 0.1%
SLC9
 
< 0.1%
HSEB7
 
< 0.1%
PABSON7
 
< 0.1%
ICSE6
 
< 0.1%
National Examination Board6
 
< 0.1%
SLC Board5
 
< 0.1%
Other values (103)123
 
0.3%
(Missing)42266
98.9%

Length

2021-05-15T10:16:24.590924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
neb189
28.6%
cbse118
17.9%
board44
 
6.7%
see26
 
3.9%
private24
 
3.6%
slc20
 
3.0%
school17
 
2.6%
of15
 
2.3%
national13
 
2.0%
examination13
 
2.0%
Other values (87)182
27.5%

Most occurring characters

ValueCountFrequency (%)
E413
 
12.4%
B368
 
11.1%
N233
 
7.0%
a226
 
6.8%
S215
 
6.5%
186
 
5.6%
o162
 
4.9%
C157
 
4.7%
i138
 
4.2%
n137
 
4.1%
Other values (46)1084
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1600
48.2%
Lowercase Letter1488
44.8%
Space Separator186
 
5.6%
Other Punctuation22
 
0.7%
Open Punctuation9
 
0.3%
Close Punctuation9
 
0.3%
Decimal Number3
 
0.1%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a226
15.2%
o162
10.9%
i138
9.3%
n137
9.2%
e115
 
7.7%
r114
 
7.7%
t104
 
7.0%
d93
 
6.2%
c60
 
4.0%
l50
 
3.4%
Other values (14)289
19.4%
Uppercase Letter
ValueCountFrequency (%)
E413
25.8%
B368
23.0%
N233
14.6%
S215
13.4%
C157
 
9.8%
P41
 
2.6%
I35
 
2.2%
A24
 
1.5%
O22
 
1.4%
L20
 
1.2%
Other values (12)72
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.15
68.2%
,5
 
22.7%
/1
 
4.5%
\1
 
4.5%
Decimal Number
ValueCountFrequency (%)
02
66.7%
11
33.3%
Space Separator
ValueCountFrequency (%)
186
100.0%
Open Punctuation
ValueCountFrequency (%)
(9
100.0%
Close Punctuation
ValueCountFrequency (%)
)9
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3088
93.0%
Common231
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E413
13.4%
B368
 
11.9%
N233
 
7.5%
a226
 
7.3%
S215
 
7.0%
o162
 
5.2%
C157
 
5.1%
i138
 
4.5%
n137
 
4.4%
e115
 
3.7%
Other values (36)924
29.9%
Common
ValueCountFrequency (%)
186
80.5%
.15
 
6.5%
(9
 
3.9%
)9
 
3.9%
,5
 
2.2%
-2
 
0.9%
02
 
0.9%
/1
 
0.4%
\1
 
0.4%
11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E413
 
12.4%
B368
 
11.1%
N233
 
7.0%
a226
 
6.8%
S215
 
6.5%
186
 
5.6%
o162
 
4.9%
C157
 
4.7%
i138
 
4.2%
n137
 
4.1%
Other values (46)1084
32.7%

PCLEquivalentSpecify
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42674
Missing (%)99.8%
Memory size334.2 KiB

PCLBoardSpecify
Categorical

HIGH CARDINALITY
MISSING

Distinct117
Distinct (%)16.0%
Missing42027
Missing (%)98.3%
Memory size334.2 KiB
NEB
558 
National Examination Board
 
11
HSBTE
 
10
BSEB
 
9
Bihar Board
 
7
Other values (112)
135 

Length

Max length69
Median length3
Mean length6.361643836
Min length1

Characters and Unicode

Total characters4644
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)13.0%

Sample

1st rowNEB
2nd rowNEB
3rd rowNEB
4th rowNational Examination Board.
5th rowNEB

Common Values

ValueCountFrequency (%)
NEB558
 
1.3%
National Examination Board11
 
< 0.1%
HSBTE10
 
< 0.1%
BSEB9
 
< 0.1%
Bihar Board7
 
< 0.1%
National Examinations Board5
 
< 0.1%
Bihar School Examination Board3
 
< 0.1%
ISC3
 
< 0.1%
CBSE3
 
< 0.1%
national examination board2
 
< 0.1%
Other values (107)119
 
0.3%
(Missing)42027
98.3%

Length

2021-05-15T10:16:24.843281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
neb563
57.3%
board84
 
8.6%
examination29
 
3.0%
national28
 
2.9%
bihar25
 
2.5%
education18
 
1.8%
of17
 
1.7%
school16
 
1.6%
technical13
 
1.3%
examinations12
 
1.2%
Other values (93)177
 
18.0%

Most occurring characters

ValueCountFrequency (%)
B718
15.5%
E672
14.5%
N620
13.4%
a372
 
8.0%
261
 
5.6%
o219
 
4.7%
i196
 
4.2%
n194
 
4.2%
t144
 
3.1%
r133
 
2.9%
Other values (44)1115
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2452
52.8%
Lowercase Letter1887
40.6%
Space Separator261
 
5.6%
Other Punctuation35
 
0.8%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Dash Punctuation3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a372
19.7%
o219
11.6%
i196
10.4%
n194
10.3%
t144
 
7.6%
r133
 
7.0%
d121
 
6.4%
l74
 
3.9%
e73
 
3.9%
c66
 
3.5%
Other values (14)295
15.6%
Uppercase Letter
ValueCountFrequency (%)
B718
29.3%
E672
27.4%
N620
25.3%
S72
 
2.9%
T56
 
2.3%
A54
 
2.2%
I50
 
2.0%
H35
 
1.4%
C32
 
1.3%
R26
 
1.1%
Other values (13)117
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.17
48.6%
,17
48.6%
&1
 
2.9%
Space Separator
ValueCountFrequency (%)
261
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4339
93.4%
Common305
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
B718
16.5%
E672
15.5%
N620
14.3%
a372
 
8.6%
o219
 
5.0%
i196
 
4.5%
n194
 
4.5%
t144
 
3.3%
r133
 
3.1%
d121
 
2.8%
Other values (37)950
21.9%
Common
ValueCountFrequency (%)
261
85.6%
.17
 
5.6%
,17
 
5.6%
(3
 
1.0%
)3
 
1.0%
-3
 
1.0%
&1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B718
15.5%
E672
14.5%
N620
13.4%
a372
 
8.0%
261
 
5.6%
o219
 
4.7%
i196
 
4.2%
n194
 
4.2%
t144
 
3.1%
r133
 
2.9%
Other values (44)1115
24.0%

SLCSchoolDistrictID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct75
Distinct (%)0.2%
Missing11138
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean31.98200449
Minimum4
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:24.964421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median31
Q350
95-th percentile69
Maximum78
Range74
Interquartile range (IQR)44

Descriptive statistics

Standard deviation23.75228948
Coefficient of variation (CV)0.7426766977
Kurtosis-1.310439518
Mean31.98200449
Median Absolute Deviation (MAD)25
Skewness0.2346443416
Sum1011239
Variance564.1712556
MonotonicityNot monotonic
2021-05-15T10:16:25.085400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45858
13.7%
62119
 
5.0%
241682
 
3.9%
431594
 
3.7%
321389
 
3.2%
51313
 
3.1%
471213
 
2.8%
421103
 
2.6%
621057
 
2.5%
54889
 
2.1%
Other values (65)13402
31.3%
(Missing)11138
26.0%
ValueCountFrequency (%)
45858
13.7%
51313
 
3.1%
62119
 
5.0%
7520
 
1.2%
8152
 
0.4%
9206
 
0.5%
1025
 
0.1%
11226
 
0.5%
12373
 
0.9%
13164
 
0.4%
ValueCountFrequency (%)
78701
1.6%
7768
 
0.2%
7666
 
0.2%
7560
 
0.1%
7437
 
0.1%
7340
 
0.1%
7243
 
0.1%
7164
 
0.1%
70202
 
0.5%
69796
1.9%

SLCSchoolFullAddress
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct504
Distinct (%)93.0%
Missing42215
Missing (%)98.7%
Memory size334.2 KiB
Chobhar, Kathmandu
 
5
Chobhar,Kathmandu
 
5
Janakpur
 
4
Kanchanpur saptari
 
3
Chobhar, Kathmandu, Nepal
 
3
Other values (499)
522 

Length

Max length137
Median length20
Mean length21.91512915
Min length1

Characters and Unicode

Total characters11878
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)88.2%

Sample

1st rowShiwalaya-5, Kushma, Parbat
2nd rowDelhi Public School, Dharan
3rd rowRupauliya-9 Mahalbari
4th rowManbhawan
5th rownarayantar,jorpati,ktm

Common Values

ValueCountFrequency (%)
Chobhar, Kathmandu5
 
< 0.1%
Chobhar,Kathmandu5
 
< 0.1%
Janakpur4
 
< 0.1%
Kanchanpur saptari3
 
< 0.1%
Chobhar, Kathmandu, Nepal3
 
< 0.1%
Malangawa, Sarlahi3
 
< 0.1%
Kathmandu3
 
< 0.1%
Bafal, Kathmandu2
 
< 0.1%
Dharan,Nepal2
 
< 0.1%
Bansbari, Kathmandu2
 
< 0.1%
Other values (494)510
 
1.2%
(Missing)42215
98.7%

Length

2021-05-15T10:16:25.367866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kathmandu62
 
4.8%
nepal38
 
2.9%
india24
 
1.9%
21
 
1.6%
lalitpur18
 
1.4%
kanchanpur17
 
1.3%
morang15
 
1.2%
school13
 
1.0%
pokhara13
 
1.0%
dharan12
 
0.9%
Other values (704)1063
82.0%

Most occurring characters

ValueCountFrequency (%)
a1949
16.4%
788
 
6.6%
r667
 
5.6%
h665
 
5.6%
n649
 
5.5%
i602
 
5.1%
,498
 
4.2%
u428
 
3.6%
t409
 
3.4%
l393
 
3.3%
Other values (61)4830
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8248
69.4%
Uppercase Letter1746
 
14.7%
Space Separator788
 
6.6%
Other Punctuation546
 
4.6%
Decimal Number373
 
3.1%
Dash Punctuation167
 
1.4%
Open Punctuation5
 
< 0.1%
Close Punctuation5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K176
 
10.1%
B168
 
9.6%
A145
 
8.3%
S129
 
7.4%
N123
 
7.0%
D108
 
6.2%
M103
 
5.9%
R99
 
5.7%
P90
 
5.2%
I87
 
5.0%
Other values (16)518
29.7%
Lowercase Letter
ValueCountFrequency (%)
a1949
23.6%
r667
 
8.1%
h665
 
8.1%
n649
 
7.9%
i602
 
7.3%
u428
 
5.2%
t409
 
5.0%
l393
 
4.8%
e290
 
3.5%
p290
 
3.5%
Other values (15)1906
23.1%
Decimal Number
ValueCountFrequency (%)
184
22.5%
083
22.3%
435
9.4%
231
 
8.3%
830
 
8.0%
525
 
6.7%
324
 
6.4%
723
 
6.2%
921
 
5.6%
617
 
4.6%
Other Punctuation
ValueCountFrequency (%)
,498
91.2%
.42
 
7.7%
/2
 
0.4%
;2
 
0.4%
:1
 
0.2%
&1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-167
100.0%
Space Separator
ValueCountFrequency (%)
788
100.0%
Open Punctuation
ValueCountFrequency (%)
(5
100.0%
Close Punctuation
ValueCountFrequency (%)
)5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9994
84.1%
Common1884
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1949
19.5%
r667
 
6.7%
h665
 
6.7%
n649
 
6.5%
i602
 
6.0%
u428
 
4.3%
t409
 
4.1%
l393
 
3.9%
e290
 
2.9%
p290
 
2.9%
Other values (41)3652
36.5%
Common
ValueCountFrequency (%)
788
41.8%
,498
26.4%
-167
 
8.9%
184
 
4.5%
083
 
4.4%
.42
 
2.2%
435
 
1.9%
231
 
1.6%
830
 
1.6%
525
 
1.3%
Other values (10)101
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1949
16.4%
788
 
6.6%
r667
 
5.6%
h665
 
5.6%
n649
 
5.5%
i602
 
5.1%
,498
 
4.2%
u428
 
3.6%
t409
 
3.4%
l393
 
3.3%
Other values (61)4830
40.7%

PCLSchoolDistrictID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)0.2%
Missing11453
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean22.78098646
Minimum4
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:25.488890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median6
Q343
95-th percentile62
Maximum78
Range74
Interquartile range (IQR)39

Descriptive statistics

Standard deviation22.49776133
Coefficient of variation (CV)0.9875674777
Kurtosis-0.9109567025
Mean22.78098646
Median Absolute Deviation (MAD)2
Skewness0.7430313014
Sum713136
Variance506.149265
MonotonicityNot monotonic
2021-05-15T10:16:25.609981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412333
28.8%
63136
 
7.3%
432342
 
5.5%
242033
 
4.8%
471505
 
3.5%
321192
 
2.8%
621154
 
2.7%
51146
 
2.7%
42978
 
2.3%
54678
 
1.6%
Other values (61)4807
 
11.2%
(Missing)11453
26.8%
ValueCountFrequency (%)
412333
28.8%
51146
 
2.7%
63136
 
7.3%
7298
 
0.7%
814
 
< 0.1%
914
 
< 0.1%
101
 
< 0.1%
1142
 
0.1%
12298
 
0.7%
1311
 
< 0.1%
ValueCountFrequency (%)
78416
1.0%
7724
 
0.1%
763
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
733
 
< 0.1%
712
 
< 0.1%
7020
 
< 0.1%
69219
0.5%
68196
0.5%

PCLSchoolFullAddress
Categorical

HIGH CARDINALITY
MISSING

Distinct619
Distinct (%)71.9%
Missing41896
Missing (%)98.0%
Memory size334.2 KiB
Maitighar, Kathmandu
 
20
Dillibazar, Kathmandu
 
10
KUMARIPATI LALITPUR
 
9
Kumaripati,Lalitpur
 
8
Kathmandu
 
8
Other values (614)
806 

Length

Max length96
Median length20
Mean length20.71196283
Min length1

Characters and Unicode

Total characters17833
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique511 ?
Unique (%)59.3%

Sample

1st rowBharatpur-9, Chitwan
2nd rowHetauda-04
3rd rowDHARAN, SUNSARI,NEPAL
4th rowBpkihs, Dharan, Nepal
5th rowhetauda-4 makwanpur

Common Values

ValueCountFrequency (%)
Maitighar, Kathmandu20
 
< 0.1%
Dillibazar, Kathmandu10
 
< 0.1%
KUMARIPATI LALITPUR9
 
< 0.1%
Kumaripati,Lalitpur8
 
< 0.1%
Kathmandu8
 
< 0.1%
Bagbazar, Kathmandu7
 
< 0.1%
New Baneshwor, Kathmandu7
 
< 0.1%
LAINCHAUR KATHMANDU7
 
< 0.1%
Basundhara, Kathmandu6
 
< 0.1%
Lainchaur, Kathmandu6
 
< 0.1%
Other values (609)773
 
1.8%
(Missing)41896
98.0%

Length

2021-05-15T10:16:25.892633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kathmandu236
 
13.5%
lalitpur55
 
3.2%
nepal41
 
2.4%
maitighar30
 
1.7%
dillibazar28
 
1.6%
india28
 
1.6%
morang24
 
1.4%
24
 
1.4%
baneshwor23
 
1.3%
new21
 
1.2%
Other values (611)1233
70.7%

Most occurring characters

ValueCountFrequency (%)
a2595
 
14.6%
h959
 
5.4%
904
 
5.1%
n849
 
4.8%
r801
 
4.5%
,779
 
4.4%
i770
 
4.3%
t759
 
4.3%
u742
 
4.2%
A702
 
3.9%
Other values (61)7973
44.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11060
62.0%
Uppercase Letter4680
26.2%
Space Separator904
 
5.1%
Other Punctuation816
 
4.6%
Decimal Number245
 
1.4%
Dash Punctuation111
 
0.6%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A702
15.0%
K491
10.5%
N348
 
7.4%
B302
 
6.5%
R294
 
6.3%
I269
 
5.7%
H268
 
5.7%
M268
 
5.7%
D263
 
5.6%
T257
 
5.5%
Other values (16)1218
26.0%
Lowercase Letter
ValueCountFrequency (%)
a2595
23.5%
h959
 
8.7%
n849
 
7.7%
r801
 
7.2%
i770
 
7.0%
t759
 
6.9%
u742
 
6.7%
d473
 
4.3%
l463
 
4.2%
m419
 
3.8%
Other values (16)2230
20.2%
Decimal Number
ValueCountFrequency (%)
165
26.5%
438
15.5%
034
13.9%
326
 
10.6%
224
 
9.8%
516
 
6.5%
615
 
6.1%
910
 
4.1%
79
 
3.7%
88
 
3.3%
Other Punctuation
ValueCountFrequency (%)
,779
95.5%
.35
 
4.3%
&1
 
0.1%
/1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-111
100.0%
Space Separator
ValueCountFrequency (%)
904
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%
Math Symbol
ValueCountFrequency (%)
<1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15740
88.3%
Common2093
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2595
 
16.5%
h959
 
6.1%
n849
 
5.4%
r801
 
5.1%
i770
 
4.9%
t759
 
4.8%
u742
 
4.7%
A702
 
4.5%
K491
 
3.1%
d473
 
3.0%
Other values (42)6599
41.9%
Common
ValueCountFrequency (%)
904
43.2%
,779
37.2%
-111
 
5.3%
165
 
3.1%
438
 
1.8%
.35
 
1.7%
034
 
1.6%
326
 
1.2%
224
 
1.1%
516
 
0.8%
Other values (9)61
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2595
 
14.6%
h959
 
5.4%
904
 
5.1%
n849
 
4.8%
r801
 
4.5%
,779
 
4.4%
i770
 
4.3%
t759
 
4.3%
u742
 
4.2%
A702
 
3.9%
Other values (61)7973
44.7%

InstitutionTypeID
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
0.0
32016 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.032016
74.9%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:26.084323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:26.134762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.032016
100.0%

Most occurring characters

ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064032
100.0%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064032
66.7%
.32016
33.3%

IdentificationTypeID
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
26117 
4.0
5389 
2.0
 
284
6.0
 
207
8.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.026117
61.1%
4.05389
 
12.6%
2.0284
 
0.7%
6.0207
 
0.5%
8.019
 
< 0.1%
(Missing)10741
25.1%

Length

2021-05-15T10:16:26.933715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:26.994272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.026117
81.6%
4.05389
 
16.8%
2.0284
 
0.9%
6.0207
 
0.6%
8.019
 
0.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
126117
27.2%
45389
 
5.6%
2284
 
0.3%
6207
 
0.2%
819
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
126117
40.8%
45389
 
8.4%
2284
 
0.4%
6207
 
0.3%
819
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
126117
27.2%
45389
 
5.6%
2284
 
0.3%
6207
 
0.2%
819
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
126117
27.2%
45389
 
5.6%
2284
 
0.3%
6207
 
0.2%
819
 
< 0.1%

IdentificationNo
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10741
Missing (%)25.1%
Memory size334.2 KiB

FormSubmittedDate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10743
Missing (%)25.1%
Memory size334.2 KiB

CheckedBy
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42757
Missing (%)100.0%
Memory size334.2 KiB

IsAccepted
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10770
Missing (%)25.2%
Memory size334.2 KiB
1.0
31973 
0.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters95961
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031973
74.8%
0.014
 
< 0.1%
(Missing)10770
 
25.2%

Length

2021-05-15T10:16:27.145684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:27.206224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031973
> 99.9%
0.014
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
032001
33.3%
.31987
33.3%
131973
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number63974
66.7%
Other Punctuation31987
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032001
50.0%
131973
50.0%
Other Punctuation
ValueCountFrequency (%)
.31987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common95961
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032001
33.3%
.31987
33.3%
131973
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII95961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032001
33.3%
.31987
33.3%
131973
33.3%

IsSubmitted
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
31184 
0.0
 
832

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.031184
72.9%
0.0832
 
1.9%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:27.347605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:27.402205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.031184
97.4%
0.0832
 
2.6%

Most occurring characters

ValueCountFrequency (%)
032848
34.2%
.32016
33.3%
131184
32.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032848
51.3%
131184
48.7%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032848
34.2%
.32016
33.3%
131184
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032848
34.2%
.32016
33.3%
131184
32.5%

Password
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct31497
Distinct (%)98.4%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
Sb36
 
3
Nk54
 
3
Kj94
 
3
j9Q4
 
3
m5T6
 
3
Other values (31492)
32001 

Length

Max length5
Median length5
Mean length4.615535982
Min length4

Characters and Unicode

Total characters147771
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30991 ?
Unique (%)96.8%

Sample

1st rowx9FT
2nd rowe6Z4
3rd rowXm6x
4th rowe9M2
5th rows2QX

Common Values

ValueCountFrequency (%)
Sb363
 
< 0.1%
Nk543
 
< 0.1%
Kj943
 
< 0.1%
j9Q43
 
< 0.1%
m5T63
 
< 0.1%
f4B93
 
< 0.1%
s2W93
 
< 0.1%
Bk253
 
< 0.1%
Yz343
 
< 0.1%
w7E23
 
< 0.1%
Other values (31487)31986
74.8%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:27.602107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
j9q43
 
< 0.1%
bk253
 
< 0.1%
nk543
 
< 0.1%
z5e83
 
< 0.1%
lj743
 
< 0.1%
f4b93
 
< 0.1%
m5t63
 
< 0.1%
kj943
 
< 0.1%
w7e23
 
< 0.1%
i9y33
 
< 0.1%
Other values (31487)31986
99.9%

Most occurring characters

ValueCountFrequency (%)
96744
 
4.6%
36703
 
4.5%
56662
 
4.5%
26646
 
4.5%
46631
 
4.5%
66593
 
4.5%
86552
 
4.4%
76469
 
4.4%
F2393
 
1.6%
D2351
 
1.6%
Other values (42)90027
60.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number53000
35.9%
Uppercase Letter49822
33.7%
Lowercase Letter44949
30.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f2217
 
4.9%
r2088
 
4.6%
o2081
 
4.6%
c2076
 
4.6%
b2061
 
4.6%
j2055
 
4.6%
s2053
 
4.6%
m2052
 
4.6%
q2050
 
4.6%
p2044
 
4.5%
Other values (12)24172
53.8%
Uppercase Letter
ValueCountFrequency (%)
F2393
 
4.8%
D2351
 
4.7%
T2349
 
4.7%
B2334
 
4.7%
E2321
 
4.7%
N2294
 
4.6%
A2291
 
4.6%
Y2281
 
4.6%
X2280
 
4.6%
W2280
 
4.6%
Other values (12)26648
53.5%
Decimal Number
ValueCountFrequency (%)
96744
12.7%
36703
12.6%
56662
12.6%
26646
12.5%
46631
12.5%
66593
12.4%
86552
12.4%
76469
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin94771
64.1%
Common53000
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
F2393
 
2.5%
D2351
 
2.5%
T2349
 
2.5%
B2334
 
2.5%
E2321
 
2.4%
N2294
 
2.4%
A2291
 
2.4%
Y2281
 
2.4%
X2280
 
2.4%
W2280
 
2.4%
Other values (34)71597
75.5%
Common
ValueCountFrequency (%)
96744
12.7%
36703
12.6%
56662
12.6%
26646
12.5%
46631
12.5%
66593
12.4%
86552
12.4%
76469
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII147771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96744
 
4.6%
36703
 
4.5%
56662
 
4.5%
26646
 
4.5%
46631
 
4.5%
66593
 
4.5%
86552
 
4.4%
76469
 
4.4%
F2393
 
1.6%
D2351
 
1.6%
Other values (42)90027
60.9%

RejectReason
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)20.0%
Missing42752
Missing (%)> 99.9%
Memory size334.2 KiB
Rejected Duplicate Entry

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters120
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRejected Duplicate Entry
2nd rowRejected Duplicate Entry
3rd rowRejected Duplicate Entry
4th rowRejected Duplicate Entry
5th rowRejected Duplicate Entry

Common Values

ValueCountFrequency (%)
Rejected Duplicate Entry5
 
< 0.1%
(Missing)42752
> 99.9%

Length

2021-05-15T10:16:27.763629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:27.824209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
entry5
33.3%
duplicate5
33.3%
rejected5
33.3%

Most occurring characters

ValueCountFrequency (%)
e20
16.7%
t15
12.5%
c10
 
8.3%
10
 
8.3%
R5
 
4.2%
j5
 
4.2%
d5
 
4.2%
D5
 
4.2%
u5
 
4.2%
p5
 
4.2%
Other values (7)35
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter95
79.2%
Uppercase Letter15
 
12.5%
Space Separator10
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e20
21.1%
t15
15.8%
c10
10.5%
j5
 
5.3%
d5
 
5.3%
u5
 
5.3%
p5
 
5.3%
l5
 
5.3%
i5
 
5.3%
a5
 
5.3%
Other values (3)15
15.8%
Uppercase Letter
ValueCountFrequency (%)
R5
33.3%
D5
33.3%
E5
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110
91.7%
Common10
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e20
18.2%
t15
13.6%
c10
 
9.1%
R5
 
4.5%
j5
 
4.5%
d5
 
4.5%
D5
 
4.5%
u5
 
4.5%
p5
 
4.5%
l5
 
4.5%
Other values (6)30
27.3%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e20
16.7%
t15
12.5%
c10
 
8.3%
10
 
8.3%
R5
 
4.2%
j5
 
4.2%
d5
 
4.2%
D5
 
4.2%
u5
 
4.2%
p5
 
4.2%
Other values (7)35
29.2%

ExamSessionName
Categorical

HIGH CARDINALITY
MISSING

Distinct155
Distinct (%)0.5%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
08/04/2017 First(07:00 AM-09:00 AM)
 
250
08/03/2017 Third(01:00 PM-03:00 PM)
 
250
08/07/2017 First(07:00 AM-09:00 AM)
 
250
08/04/2017 Third(01:00 PM-03:00 PM)
 
250
08/05/2017 First(07:00 AM-09:00 AM)
 
250
Other values (150)
30762 

Length

Max length37
Median length37
Mean length36.5134637
Min length36

Characters and Unicode

Total characters1168869
Distinct characters33
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07/30/2017 Second(10:00 AM-12:00 PM)
2nd row08/03/2017 Second(10:00 AM-12:00 PM)
3rd row08/07/2017 First(07:00 AM-09:00 AM)
4th row08/06/2017 Second(10:00 AM-12:00 PM)
5th row08/07/2017 Second(10:00 AM-12:00 PM)

Common Values

ValueCountFrequency (%)
08/04/2017 First(07:00 AM-09:00 AM) 250
 
0.6%
08/03/2017 Third(01:00 PM-03:00 PM) 250
 
0.6%
08/07/2017 First(07:00 AM-09:00 AM) 250
 
0.6%
08/04/2017 Third(01:00 PM-03:00 PM) 250
 
0.6%
08/05/2017 First(07:00 AM-09:00 AM) 250
 
0.6%
08/05/2017 Third(01:00 PM-03:00 PM) 250
 
0.6%
07/30/2017 Second(10:00 AM-12:00 PM) 250
 
0.6%
08/10/2017 Second(10:00 AM-12:00 PM) 250
 
0.6%
08/10/2017 First(07:00 AM-09:00 AM) 250
 
0.6%
07/30/2017 Fourth(04:00 PM-06:00 PM) 250
 
0.6%
Other values (145)29512
69.0%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:28.015869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm24093
18.8%
second(10:008622
 
6.7%
am-12:008622
 
6.7%
first(07:007919
 
6.2%
am-09:007919
 
6.2%
am7919
 
6.2%
pm-06:007815
 
6.1%
fourth(04:007815
 
6.1%
third(01:007656
 
6.0%
pm-03:007656
 
6.0%
Other values (41)32012
25.0%

Most occurring characters

ValueCountFrequency (%)
0271270
23.2%
128048
 
11.0%
172668
 
6.2%
266807
 
5.7%
/64024
 
5.5%
:64024
 
5.5%
M64024
 
5.5%
P39564
 
3.4%
(32012
 
2.7%
-32012
 
2.7%
Other values (23)334416
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number512192
43.8%
Uppercase Letter160060
 
13.7%
Lowercase Letter144485
 
12.4%
Other Punctuation128048
 
11.0%
Space Separator128048
 
11.0%
Open Punctuation32012
 
2.7%
Dash Punctuation32012
 
2.7%
Close Punctuation32012
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r23390
16.2%
o16437
11.4%
d16278
11.3%
t15734
10.9%
i15575
10.8%
h15471
10.7%
e8622
 
6.0%
c8622
 
6.0%
n8622
 
6.0%
s7919
 
5.5%
Decimal Number
ValueCountFrequency (%)
0271270
53.0%
172668
 
14.2%
266807
 
13.0%
724830
 
4.8%
821352
 
4.2%
918497
 
3.6%
313430
 
2.6%
610649
 
2.1%
410143
 
2.0%
52546
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
M64024
40.0%
P39564
24.7%
A24460
 
15.3%
F15734
 
9.8%
S8622
 
5.4%
T7656
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/64024
50.0%
:64024
50.0%
Space Separator
ValueCountFrequency (%)
128048
100.0%
Open Punctuation
ValueCountFrequency (%)
(32012
100.0%
Dash Punctuation
ValueCountFrequency (%)
-32012
100.0%
Close Punctuation
ValueCountFrequency (%)
)32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common864324
73.9%
Latin304545
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
M64024
21.0%
P39564
13.0%
A24460
 
8.0%
r23390
 
7.7%
o16437
 
5.4%
d16278
 
5.3%
F15734
 
5.2%
t15734
 
5.2%
i15575
 
5.1%
h15471
 
5.1%
Other values (7)57878
19.0%
Common
ValueCountFrequency (%)
0271270
31.4%
128048
14.8%
172668
 
8.4%
266807
 
7.7%
/64024
 
7.4%
:64024
 
7.4%
(32012
 
3.7%
-32012
 
3.7%
)32012
 
3.7%
724830
 
2.9%
Other values (6)76617
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1168869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0271270
23.2%
128048
 
11.0%
172668
 
6.2%
266807
 
5.7%
/64024
 
5.5%
:64024
 
5.5%
M64024
 
5.5%
P39564
 
3.4%
(32012
 
2.7%
-32012
 
2.7%
Other values (23)334416
28.6%

ExamSessionDateAD
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10745
Missing (%)25.1%
Memory size334.2 KiB

ExamSessionDateBS
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10745
Missing (%)25.1%
Memory size334.2 KiB

Capacity
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
250.0
19304 
225.0
12708 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters160060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250.0
2nd row250.0
3rd row250.0
4th row250.0
5th row250.0

Common Values

ValueCountFrequency (%)
250.019304
45.1%
225.012708
29.7%
(Missing)10745
25.1%

Length

2021-05-15T10:16:28.187476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:28.248031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
250.019304
60.3%
225.012708
39.7%

Most occurring characters

ValueCountFrequency (%)
051316
32.1%
244720
27.9%
532012
20.0%
.32012
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number128048
80.0%
Other Punctuation32012
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
051316
40.1%
244720
34.9%
532012
25.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common160060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
051316
32.1%
244720
27.9%
532012
20.0%
.32012
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII160060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
051316
32.1%
244720
27.9%
532012
20.0%
.32012
20.0%

ExamDurationMinute
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
120.0
32012 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters160060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120.0
2nd row120.0
3rd row120.0
4th row120.0
5th row120.0

Common Values

ValueCountFrequency (%)
120.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:28.399451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:28.449912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
120.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
064024
40.0%
132012
20.0%
232012
20.0%
.32012
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number128048
80.0%
Other Punctuation32012
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064024
50.0%
132012
25.0%
232012
25.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common160060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064024
40.0%
132012
20.0%
232012
20.0%
.32012
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII160060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064024
40.0%
132012
20.0%
232012
20.0%
.32012
20.0%

IsResultImmediately
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
0.0
32012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96036
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:28.591248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:28.641696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
064024
66.7%
.32012
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64024
66.7%
Other Punctuation32012
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064024
100.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064024
66.7%
.32012
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064024
66.7%
.32012
33.3%

PageSize
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
20.0
32012 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters128048
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row20.0
4th row20.0
5th row20.0

Common Values

ValueCountFrequency (%)
20.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:28.783007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:28.833457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
064024
50.0%
232012
25.0%
.32012
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number96036
75.0%
Other Punctuation32012
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064024
66.7%
232012
33.3%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common128048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064024
50.0%
232012
25.0%
.32012
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII128048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064024
50.0%
232012
25.0%
.32012
25.0%

ShiftName
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
Second
8622 
First
7919 
Fourth
7815 
Third
7656 

Length

Max length6
Median length6
Mean length5.513463701
Min length5

Characters and Unicode

Total characters176497
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond
2nd rowSecond
3rd rowFirst
4th rowSecond
5th rowSecond

Common Values

ValueCountFrequency (%)
Second8622
20.2%
First7919
18.5%
Fourth7815
18.3%
Third7656
17.9%
(Missing)10745
25.1%

Length

2021-05-15T10:16:28.985001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:29.045544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
second8622
26.9%
first7919
24.7%
fourth7815
24.4%
third7656
23.9%

Most occurring characters

ValueCountFrequency (%)
r23390
13.3%
o16437
9.3%
d16278
9.2%
F15734
8.9%
t15734
8.9%
i15575
8.8%
h15471
8.8%
S8622
 
4.9%
e8622
 
4.9%
c8622
 
4.9%
Other values (4)32012
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter144485
81.9%
Uppercase Letter32012
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r23390
16.2%
o16437
11.4%
d16278
11.3%
t15734
10.9%
i15575
10.8%
h15471
10.7%
e8622
 
6.0%
c8622
 
6.0%
n8622
 
6.0%
s7919
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F15734
49.2%
S8622
26.9%
T7656
23.9%

Most occurring scripts

ValueCountFrequency (%)
Latin176497
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r23390
13.3%
o16437
9.3%
d16278
9.2%
F15734
8.9%
t15734
8.9%
i15575
8.8%
h15471
8.8%
S8622
 
4.9%
e8622
 
4.9%
c8622
 
4.9%
Other values (4)32012
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII176497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r23390
13.3%
o16437
9.3%
d16278
9.2%
F15734
8.9%
t15734
8.9%
i15575
8.8%
h15471
8.8%
S8622
 
4.9%
e8622
 
4.9%
c8622
 
4.9%
Other values (4)32012
18.1%

StartTime
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10745
Missing (%)25.1%
Memory size334.2 KiB

EndTime
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10745
Missing (%)25.1%
Memory size334.2 KiB

FormStatus
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
2.0
31973 
1.0
 
29
3.0
 
12
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.031973
74.8%
1.029
 
0.1%
3.012
 
< 0.1%
4.02
 
< 0.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:29.206872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:29.265390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.031973
99.9%
1.029
 
0.1%
3.012
 
< 0.1%
4.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
231973
33.3%
129
 
< 0.1%
312
 
< 0.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032016
50.0%
231973
49.9%
129
 
< 0.1%
312
 
< 0.1%
42
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
231973
33.3%
129
 
< 0.1%
312
 
< 0.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32016
33.3%
032016
33.3%
231973
33.3%
129
 
< 0.1%
312
 
< 0.1%
42
 
< 0.1%

HasStudentAttemptedExam
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
19250 
0.0
12766 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.019250
45.0%
0.012766
29.9%
(Missing)10741
25.1%

Length

2021-05-15T10:16:29.418769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:29.469177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.019250
60.1%
0.012766
39.9%

Most occurring characters

ValueCountFrequency (%)
044782
46.6%
.32016
33.3%
119250
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
044782
69.9%
119250
30.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
044782
46.6%
.32016
33.3%
119250
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
044782
46.6%
.32016
33.3%
119250
20.0%

FormIndex
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12765
Distinct (%)39.9%
Missing10743
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean6024.121947
Minimum1
Maximum12765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:29.547850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile569
Q12893
median5918
Q39075.75
95-th percentile11809.35
Maximum12765
Range12764
Interquartile range (IQR)6182.75

Descriptive statistics

Standard deviation3597.944618
Coefficient of variation (CV)0.5972562724
Kurtosis-1.173628843
Mean6024.121947
Median Absolute Deviation (MAD)3087
Skewness0.08225620629
Sum192856240
Variance12945205.48
MonotonicityNot monotonic
2021-05-15T10:16:29.670991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29813
 
< 0.1%
3193
 
< 0.1%
45483
 
< 0.1%
52573
 
< 0.1%
75353
 
< 0.1%
70653
 
< 0.1%
26543
 
< 0.1%
73363
 
< 0.1%
74693
 
< 0.1%
89393
 
< 0.1%
Other values (12755)31984
74.8%
(Missing)10743
 
25.1%
ValueCountFrequency (%)
13
< 0.1%
22
< 0.1%
33
< 0.1%
42
< 0.1%
53
< 0.1%
63
< 0.1%
73
< 0.1%
83
< 0.1%
93
< 0.1%
103
< 0.1%
ValueCountFrequency (%)
127651
< 0.1%
127641
< 0.1%
127631
< 0.1%
127621
< 0.1%
127611
< 0.1%
127601
< 0.1%
127591
< 0.1%
127581
< 0.1%
127571
< 0.1%
127561
< 0.1%

FacultyID
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
2.0
32012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96036
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:29.862772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:29.931290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
232012
33.3%
.32012
33.3%
032012
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64024
66.7%
Other Punctuation32012
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
232012
50.0%
032012
50.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
232012
33.3%
.32012
33.3%
032012
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232012
33.3%
.32012
33.3%
032012
33.3%

LevelID
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
1.0
32012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96036
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.032012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:30.092299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:30.144878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.032012
100.0%

Most occurring characters

ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64024
66.7%
Other Punctuation32012
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132012
50.0%
032012
50.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132012
33.3%
.32012
33.3%
032012
33.3%

ShiftID
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
2.0
8622 
1.0
7919 
4.0
7815 
3.0
7656 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96036
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.08622
20.2%
1.07919
18.5%
4.07815
18.3%
3.07656
17.9%
(Missing)10745
25.1%

Length

2021-05-15T10:16:30.286124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:30.354757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.08622
26.9%
1.07919
24.7%
4.07815
24.4%
3.07656
23.9%

Most occurring characters

ValueCountFrequency (%)
.32012
33.3%
032012
33.3%
28622
 
9.0%
17919
 
8.2%
47815
 
8.1%
37656
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64024
66.7%
Other Punctuation32012
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032012
50.0%
28622
 
13.5%
17919
 
12.4%
47815
 
12.2%
37656
 
12.0%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96036
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.32012
33.3%
032012
33.3%
28622
 
9.0%
17919
 
8.2%
47815
 
8.1%
37656
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII96036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.32012
33.3%
032012
33.3%
28622
 
9.0%
17919
 
8.2%
47815
 
8.1%
37656
 
8.0%

FiscalYearID
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
19.0
19707 
17.0
12305 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters128048
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17.0
2nd row17.0
3rd row17.0
4th row17.0
5th row17.0

Common Values

ValueCountFrequency (%)
19.019707
46.1%
17.012305
28.8%
(Missing)10745
25.1%

Length

2021-05-15T10:16:30.518291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:30.568751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
19.019707
61.6%
17.012305
38.4%

Most occurring characters

ValueCountFrequency (%)
132012
25.0%
.32012
25.0%
032012
25.0%
919707
15.4%
712305
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number96036
75.0%
Other Punctuation32012
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132012
33.3%
032012
33.3%
919707
20.5%
712305
 
12.8%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common128048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
132012
25.0%
.32012
25.0%
032012
25.0%
919707
15.4%
712305
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII128048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
132012
25.0%
.32012
25.0%
032012
25.0%
919707
15.4%
712305
 
9.6%

FacultyName
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
Engineering
32012 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters352132
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngineering
2nd rowEngineering
3rd rowEngineering
4th rowEngineering
5th rowEngineering

Common Values

ValueCountFrequency (%)
Engineering32012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:30.710013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:30.770549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
engineering32012
100.0%

Most occurring characters

ValueCountFrequency (%)
n96036
27.3%
g64024
18.2%
i64024
18.2%
e64024
18.2%
E32012
 
9.1%
r32012
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter320120
90.9%
Uppercase Letter32012
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n96036
30.0%
g64024
20.0%
i64024
20.0%
e64024
20.0%
r32012
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
E32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin352132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n96036
27.3%
g64024
18.2%
i64024
18.2%
e64024
18.2%
E32012
 
9.1%
r32012
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII352132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n96036
27.3%
g64024
18.2%
i64024
18.2%
e64024
18.2%
E32012
 
9.1%
r32012
 
9.1%

LevelName
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
BE / B.Arch.
32012 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters384144
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBE / B.Arch.
2nd rowBE / B.Arch.
3rd rowBE / B.Arch.
4th rowBE / B.Arch.
5th rowBE / B.Arch.

Common Values

ValueCountFrequency (%)
BE / B.Arch.32012
74.9%
(Missing)10745
 
25.1%

Length

2021-05-15T10:16:30.909801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:30.962336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
32012
33.3%
b.arch32012
33.3%
be32012
33.3%

Most occurring characters

ValueCountFrequency (%)
B64024
16.7%
64024
16.7%
.64024
16.7%
E32012
8.3%
/32012
8.3%
A32012
8.3%
r32012
8.3%
c32012
8.3%
h32012
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter128048
33.3%
Other Punctuation96036
25.0%
Lowercase Letter96036
25.0%
Space Separator64024
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B64024
50.0%
E32012
25.0%
A32012
25.0%
Lowercase Letter
ValueCountFrequency (%)
r32012
33.3%
c32012
33.3%
h32012
33.3%
Other Punctuation
ValueCountFrequency (%)
.64024
66.7%
/32012
33.3%
Space Separator
ValueCountFrequency (%)
64024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224084
58.3%
Common160060
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
B64024
28.6%
E32012
14.3%
A32012
14.3%
r32012
14.3%
c32012
14.3%
h32012
14.3%
Common
ValueCountFrequency (%)
64024
40.0%
.64024
40.0%
/32012
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII384144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B64024
16.7%
64024
16.7%
.64024
16.7%
E32012
8.3%
/32012
8.3%
A32012
8.3%
r32012
8.3%
c32012
8.3%
h32012
8.3%

FiscalYearName
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing10745
Missing (%)25.1%
Memory size334.2 KiB
2077.0
12708 
2074.0
12305 
2076.0
6999 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters192072
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2074.0
2nd row2074.0
3rd row2074.0
4th row2074.0
5th row2074.0

Common Values

ValueCountFrequency (%)
2077.012708
29.7%
2074.012305
28.8%
2076.06999
16.4%
(Missing)10745
25.1%

Length

2021-05-15T10:16:31.113738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:31.164331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2077.012708
39.7%
2074.012305
38.4%
2076.06999
21.9%

Most occurring characters

ValueCountFrequency (%)
064024
33.3%
744720
23.3%
232012
16.7%
.32012
16.7%
412305
 
6.4%
66999
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160060
83.3%
Other Punctuation32012
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
064024
40.0%
744720
27.9%
232012
20.0%
412305
 
7.7%
66999
 
4.4%
Other Punctuation
ValueCountFrequency (%)
.32012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common192072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
064024
33.3%
744720
23.3%
232012
16.7%
.32012
16.7%
412305
 
6.4%
66999
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII192072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
064024
33.3%
744720
23.3%
232012
16.7%
.32012
16.7%
412305
 
6.4%
66999
 
3.6%

RollNoString
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42757
Missing (%)100.0%
Memory size334.2 KiB

PhotoDocumentID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32016
Distinct (%)100.0%
Missing10741
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean195489.048
Minimum106854
Maximum287938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.2 KiB
2021-05-15T10:16:31.245594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum106854
5-th percentile112389.5
Q1131597.25
median203064.5
Q3249434.25
95-th percentile279441.5
Maximum287938
Range181084
Interquartile range (IQR)117837

Descriptive statistics

Standard deviation60084.72656
Coefficient of variation (CV)0.307355973
Kurtosis-1.544674968
Mean195489.048
Median Absolute Deviation (MAD)62120
Skewness-0.08682372072
Sum6258777361
Variance3610174365
MonotonicityNot monotonic
2021-05-15T10:16:31.367900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2762201
 
< 0.1%
2395391
 
< 0.1%
1885191
 
< 0.1%
1900081
 
< 0.1%
2629601
 
< 0.1%
2634991
 
< 0.1%
1901861
 
< 0.1%
1409961
 
< 0.1%
1363491
 
< 0.1%
1270201
 
< 0.1%
Other values (32006)32006
74.9%
(Missing)10741
 
25.1%
ValueCountFrequency (%)
1068541
< 0.1%
1068601
< 0.1%
1068651
< 0.1%
1068681
< 0.1%
1068711
< 0.1%
1068741
< 0.1%
1068771
< 0.1%
1068821
< 0.1%
1068851
< 0.1%
1068911
< 0.1%
ValueCountFrequency (%)
2879381
< 0.1%
2879351
< 0.1%
2879321
< 0.1%
2879291
< 0.1%
2879261
< 0.1%
2879231
< 0.1%
2879201
< 0.1%
2879171
< 0.1%
2879131
< 0.1%
2879101
< 0.1%

ExamRollNo
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42757
Missing (%)100.0%
Memory size334.2 KiB

Active
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
1.0
32014 
0.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.032014
74.9%
0.02
 
< 0.1%
(Missing)10741
 
25.1%

Length

2021-05-15T10:16:31.581807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:31.642857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.032014
> 99.9%
0.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
032018
33.3%
.32016
33.3%
132014
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032018
50.0%
132014
50.0%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032018
33.3%
.32016
33.3%
132014
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032018
33.3%
.32016
33.3%
132014
33.3%

ExamStartedTime
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing11569
Missing (%)27.1%
Memory size334.2 KiB

CreatedBy
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing10741
Missing (%)25.1%
Memory size334.2 KiB
2.0
24370 
0.0
7646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters96048
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.024370
57.0%
0.07646
 
17.9%
(Missing)10741
25.1%

Length

2021-05-15T10:16:31.804943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:31.865599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.024370
76.1%
0.07646
 
23.9%

Most occurring characters

ValueCountFrequency (%)
039662
41.3%
.32016
33.3%
224370
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64032
66.7%
Other Punctuation32016
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039662
61.9%
224370
38.1%
Other Punctuation
ValueCountFrequency (%)
.32016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common96048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039662
41.3%
.32016
33.3%
224370
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII96048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039662
41.3%
.32016
33.3%
224370
25.4%

CreatedDate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10741
Missing (%)25.1%
Memory size334.2 KiB

ModifiedBy
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.1%
Missing35537
Missing (%)83.1%
Memory size334.2 KiB
2.0
3018 
1.0
1697 
9.0
1315 
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21660
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.03018
 
7.1%
1.01697
 
4.0%
9.01315
 
3.1%
0.01190
 
2.8%
(Missing)35537
83.1%

Length

2021-05-15T10:16:32.037380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:32.108143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.03018
41.8%
1.01697
23.5%
9.01315
18.2%
0.01190
 
16.5%

Most occurring characters

ValueCountFrequency (%)
08410
38.8%
.7220
33.3%
23018
 
13.9%
11697
 
7.8%
91315
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14440
66.7%
Other Punctuation7220
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08410
58.2%
23018
 
20.9%
11697
 
11.8%
91315
 
9.1%
Other Punctuation
ValueCountFrequency (%)
.7220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21660
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08410
38.8%
.7220
33.3%
23018
 
13.9%
11697
 
7.8%
91315
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII21660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08410
38.8%
.7220
33.3%
23018
 
13.9%
11697
 
7.8%
91315
 
6.1%

ModifiedDate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing35537
Missing (%)83.1%
Memory size334.2 KiB

SLCBoardName
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing30049
Missing (%)70.3%
Memory size334.2 KiB
Nepal Gov
12505 
Other
 
203

Length

Max length9
Median length9
Mean length8.936103242
Min length5

Characters and Unicode

Total characters113560
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNepal Gov
2nd rowNepal Gov
3rd rowNepal Gov
4th rowNepal Gov
5th rowNepal Gov

Common Values

ValueCountFrequency (%)
Nepal Gov12505
29.2%
Other203
 
0.5%
(Missing)30049
70.3%

Length

2021-05-15T10:16:32.279927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:32.348563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
gov12505
49.6%
nepal12505
49.6%
other203
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e12708
11.2%
N12505
11.0%
p12505
11.0%
a12505
11.0%
l12505
11.0%
12505
11.0%
G12505
11.0%
o12505
11.0%
v12505
11.0%
O203
 
0.2%
Other values (3)609
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter75842
66.8%
Uppercase Letter25213
 
22.2%
Space Separator12505
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12708
16.8%
p12505
16.5%
a12505
16.5%
l12505
16.5%
o12505
16.5%
v12505
16.5%
t203
 
0.3%
h203
 
0.3%
r203
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N12505
49.6%
G12505
49.6%
O203
 
0.8%
Space Separator
ValueCountFrequency (%)
12505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101055
89.0%
Common12505
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12708
12.6%
N12505
12.4%
p12505
12.4%
a12505
12.4%
l12505
12.4%
G12505
12.4%
o12505
12.4%
v12505
12.4%
O203
 
0.2%
t203
 
0.2%
Other values (2)406
 
0.4%
Common
ValueCountFrequency (%)
12505
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12708
11.2%
N12505
11.0%
p12505
11.0%
a12505
11.0%
l12505
11.0%
12505
11.0%
G12505
11.0%
o12505
11.0%
v12505
11.0%
O203
 
0.2%
Other values (3)609
 
0.5%

PCLBoardName
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing30049
Missing (%)70.3%
Memory size334.2 KiB
HSEB/NEB
12127 
CTEVT
 
294
Cambridge University
 
140
Other
 
67
CBSE
 
47

Length

Max length20
Median length8
Mean length8.016603714
Min length2

Characters and Unicode

Total characters101875
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHSEB/NEB
2nd rowHSEB/NEB
3rd rowHSEB/NEB
4th rowHSEB/NEB
5th rowHSEB/NEB

Common Values

ValueCountFrequency (%)
HSEB/NEB12127
28.4%
CTEVT294
 
0.7%
Cambridge University140
 
0.3%
Other67
 
0.2%
CBSE47
 
0.1%
TU33
 
0.1%
(Missing)30049
70.3%

Length

2021-05-15T10:16:32.502145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T10:16:32.572852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
hseb/neb12127
94.4%
ctevt294
 
2.3%
university140
 
1.1%
cambridge140
 
1.1%
other67
 
0.5%
cbse47
 
0.4%
tu33
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E24595
24.1%
B24301
23.9%
S12174
11.9%
H12127
11.9%
/12127
11.9%
N12127
11.9%
T621
 
0.6%
C481
 
0.5%
i420
 
0.4%
e347
 
0.3%
Other values (16)2555
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter86960
85.4%
Other Punctuation12127
 
11.9%
Lowercase Letter2648
 
2.6%
Space Separator140
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i420
15.9%
e347
13.1%
r347
13.1%
t207
7.8%
a140
 
5.3%
m140
 
5.3%
b140
 
5.3%
d140
 
5.3%
g140
 
5.3%
n140
 
5.3%
Other values (4)487
18.4%
Uppercase Letter
ValueCountFrequency (%)
E24595
28.3%
B24301
27.9%
S12174
14.0%
H12127
13.9%
N12127
13.9%
T621
 
0.7%
C481
 
0.6%
V294
 
0.3%
U173
 
0.2%
O67
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/12127
100.0%
Space Separator
ValueCountFrequency (%)
140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin89608
88.0%
Common12267
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E24595
27.4%
B24301
27.1%
S12174
13.6%
H12127
13.5%
N12127
13.5%
T621
 
0.7%
C481
 
0.5%
i420
 
0.5%
e347
 
0.4%
r347
 
0.4%
Other values (14)2068
 
2.3%
Common
ValueCountFrequency (%)
/12127
98.9%
140
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII101875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E24595
24.1%
B24301
23.9%
S12174
11.9%
H12127
11.9%
/12127
11.9%
N12127
11.9%
T621
 
0.6%
C481
 
0.5%
i420
 
0.4%
e347
 
0.3%
Other values (16)2555
 
2.5%

Interactions

2021-05-15T10:15:25.141946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.257300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.373158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.473425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.573697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.696574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.796841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:25.900856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.047094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.161172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.272176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.395186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.522327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.622623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.738589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:26.869272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.020870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.161529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.316076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.473660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.623254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.724231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:27.838501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.093057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.219445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.331055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.471397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.603008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.730310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.860960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:28.985553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.156524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.287894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.425950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.552941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.674590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.796098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:29.897347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.008499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.109815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.199465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.312534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.413649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.513119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.617057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.718008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.819493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:30.920341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.031205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.142092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.250977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.354483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.466584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.578466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.677208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.778211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.889235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:31.980124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.083489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.183187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.284162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.386966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.495919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.606786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.707596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.810474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:32.911414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.013309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.102859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.203824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.315655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.416607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.521117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.633151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:33.742308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.015414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.116204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.239452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.351352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.450197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.573248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.684103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.795837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:34.905043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.006916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.118402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.232316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.344307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.455305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.566458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.688472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.799588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:35.910640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.009612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.120596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.233556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.345546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.446504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.557817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.669813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.778986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.882977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:36.992073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.096005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.205177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.309179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.421380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.533417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.644611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.746454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.845414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:37.937181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.030298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.131362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.231239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.324917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.423742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.516490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.606965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.697949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.790298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.879153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:38.970203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.073028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.162647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.253545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.354488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.457446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.568484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.669441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.770718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.881573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:39.992550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.093777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.202827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.303850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.416841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.517780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.829185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:40.930186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.041212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.144056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.254889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.353830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.466917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.576108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.690258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.791220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:41.892223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.003271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.114781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.213629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.324654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.436114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.539087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.650196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.762257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.873338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:42.974300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.085329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.196186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.307167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.418566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.529574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.630515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.731439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.830258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:43.933143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.041878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.135414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.237250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.346400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.450304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.551305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.652237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.751637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.854511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:44.965427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.075521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.179217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.290459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.392441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.502385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.595271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.684125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.798173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.899365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:45.990918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.092070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.203995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.315578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.424302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.540200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.647121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.757965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.868937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:46.979996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.090949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.201978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.321458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.432562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.523498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.626594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.738549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.847577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:47.950693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.062160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.173232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.284311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.385707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.496771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.607764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.718862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.830857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:48.941871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.052884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.416839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.527896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.647831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.751778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.853588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:49.964608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.073598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.164568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.277998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.388963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.500444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.609390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.712329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.823440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:50.934512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.043489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.156346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.267343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.378630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.480429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.591454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.692357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.781269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.894387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:51.995283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.086531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.187451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.296526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.398258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.499233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.600135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.701697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.795469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:52.904672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.016708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.117617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.220698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.332360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.454577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.556160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.657004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.767923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.879788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:53.980851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.092875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.204105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.316230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.418020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.529177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.640801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.749744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.862854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:54.973911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.084988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.196012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.307020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.427243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.530398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.631591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.743540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.854640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:55.955526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.066190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.177191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.288258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.400115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.511264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.623471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.745605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.856763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:56.969949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.081832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.192886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.303945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.415000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.514175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.614979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.728189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.829451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:57.928383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.031298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-05-15T10:15:58.354884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.455946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.566048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.669928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.781048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:58.892316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-05-15T10:15:59.226433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:59.347660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:59.456852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:15:59.559851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.004354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.115526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.206879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.307733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.426845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.537890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.648946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.752469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.873728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:00.974696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:01.093759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:01.206831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T10:16:01.317754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-15T10:16:32.734410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-15T10:16:33.168346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-15T10:16:33.590510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-15T10:16:34.006573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-15T10:16:01.932824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-15T10:16:06.163200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-15T10:16:09.444758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-15T10:16:12.393285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

FormNoFirstNameMiddleNameLastNameMunicipalityVdcWardNoGenderBirthDateBSBirthDateADSLCSchoolNameSLCSymbolNoSLCPassedYearSLCPassedYearCalendarSLCPercentagePCLSymbolNoPCLSchoolNamePCLPercentagePCLPassedYearPCLPassedYearCalendarEthnicGroupIDFatherFirstNameFatherMiddleNameFatherLastNameMotherFirstNameMotherMiddleNameMotherLastNameDistrictCodeDistrictNameNationalityNameIdentificationTypeNameFullNameEntranceScoreEntranceRankdateStudentIDVoucherNoStudentCodeContactNoEmailNationalityIDCountryIDIndianEmbassyIDExamCenterIDExamSessionIDZoneIDDistrictIDSLCEquivalentIDSLCBoardIDPCLEquivalentIDPCLBoardIDPCLResultTypeIDPCLLocationIDEthnicGroupSpecifySLCEquivalentSpecifySLCBoardSpecifyPCLEquivalentSpecifyPCLBoardSpecifySLCSchoolDistrictIDSLCSchoolFullAddressPCLSchoolDistrictIDPCLSchoolFullAddressInstitutionTypeIDIdentificationTypeIDIdentificationNoFormSubmittedDateCheckedByIsAcceptedIsSubmittedPasswordRejectReasonExamSessionNameExamSessionDateADExamSessionDateBSCapacityExamDurationMinuteIsResultImmediatelyPageSizeShiftNameStartTimeEndTimeFormStatusHasStudentAttemptedExamFormIndexFacultyIDLevelIDShiftIDFiscalYearIDFacultyNameLevelNameFiscalYearNameRollNoStringPhotoDocumentIDExamRollNoActiveExamStartedTimeCreatedByCreatedDateModifiedByModifiedDateSLCBoardNamePCLBoardName
02073-1AashutoshNaNAdhikariChitrawan8.0Male2056-04-19 00:00:001999-08-04 00:00:00Triyog Higher Secondary School0316797M2070BS89.8827612125Liverpool International CollegeNaNNaNBSNaNPitriBhaktaAdhikariSubhadraNaNWagle35.0ChitawanNepaliCitizenshipAashutosh Adhikari73.286245.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12073-2BikalNaNYonjanBishnupurkatti1.0Male2053-04-13 00:00:001996-07-28 00:00:00Greenland English Boarding School0166329 G2068BS69.7522712320The New Summit H.S School65.12071.0BSNaNBhailalNaNYonjanLalitaKumariLama16.0SirahaNepaliCitizenshipBikal YonjanNaNNaN2073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
22073-3ShramikNaNRimalBidur8.0Male2055-01-18 00:00:001998-05-01 00:00:00Bagmati Boarding H.S. School310622Z2070BS87.5027600939Kathmandu Model H.S. SchoolNaNNaNBSNaNKrishnaPrasadRimalSitaAdhikariRimal28.0NuwakotNepaliCitizenshipShramik RimalNaNNaN2073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32073-4SHISHIRNaNKHADKABhimeshwor6.0Male2055-08-05 00:00:001998-11-21 00:00:00Sainik Awasiya Mahavidhyalaya0281744 H2070BS84.8822602033Sainik Awasiya Mahavidhyalaya H S SchoolNaNNaNBSNaNShivaBahadurKhadkaSirjanaNaNKhadka22.0DolakhaNepaliCitizenshipSHISHIR KHADKA45.7863327.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
42073-5durgarajnepalprangbung4.0Male2053-08-05 00:00:001996-11-20 00:00:00bud international public school0049789y2070BS72.0020400853COHEDNaNNaNBSNaNdeviprasadnepalmenukaNaNnepal2.0PanchtharNepaliCitizenshipdurga raj nepalNaNNaN2073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
52073-7SauravSagarRayPhulahatta Parikauli7.0Male2056-03-04 00:00:001999-06-18 00:00:00REHDON Higher Secondary School0300695E2070BS84.1327600387Kathmandu Model Higher Secondary SchoolNaNNaNBSNaNPashupatiKumarRayPushpaKumariSingh18.0MahottariNepaliCitizenshipSaurav Sagar Ray63.071763.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
62073-6SURAJNaNBAIDYABHAKTAPUR6.0Male2056-09-02 00:00:001999-12-17 00:00:00OM SECONDARY SCHOOL0281356J2070BS69.3822703843V S NIKETAN H S SNaNNaNBSNaNSURENDRALALBAIDYARAJANINaNBAIDYA26.0BhaktapurNepaliCitizenshipSURAJ BAIDYANaNNaN2073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
72073-9KashiramNaNKhadkaKhalanga5.0Male2053-04-26 00:00:001996-08-10 00:00:00Shree Tribhuvan Janata H.S.S0540042 V2069BS69.2522700366National School Of Science67.32072.0BSNaNKumbirNaNKhadkaAaitiNaNkhadka54.0RukumNepaliCitizenshipKashiram Khadka61.429907.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
82073-8rohitNaNrajbanshibirtamod9.0Male2055-05-12 00:00:001998-08-28 00:00:00birat jyoti english boarding secondary higher school0047664f2070BS76.7420400879COHEDNaNNaNBSNaNdhanbahadurrajbanshipadmaNaNrajbanshi4.0JhapaNepaliCitizenshiprohit rajbanshi48.1432830.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
92073-10BIPLABNaNKARKIMadhya Nepal7.0Male2056-05-21 00:00:001999-09-06 00:00:00Bright Future Secondary School0299388X2070BS88.7522700290National school of SciencesNaNNaNBSNaNShivaNaNKarkiBimalaDeviKarki37.0LamjungNepaliCitizenshipBIPLAB KARKI76.429152.02073NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

FormNoFirstNameMiddleNameLastNameMunicipalityVdcWardNoGenderBirthDateBSBirthDateADSLCSchoolNameSLCSymbolNoSLCPassedYearSLCPassedYearCalendarSLCPercentagePCLSymbolNoPCLSchoolNamePCLPercentagePCLPassedYearPCLPassedYearCalendarEthnicGroupIDFatherFirstNameFatherMiddleNameFatherLastNameMotherFirstNameMotherMiddleNameMotherLastNameDistrictCodeDistrictNameNationalityNameIdentificationTypeNameFullNameEntranceScoreEntranceRankdateStudentIDVoucherNoStudentCodeContactNoEmailNationalityIDCountryIDIndianEmbassyIDExamCenterIDExamSessionIDZoneIDDistrictIDSLCEquivalentIDSLCBoardIDPCLEquivalentIDPCLBoardIDPCLResultTypeIDPCLLocationIDEthnicGroupSpecifySLCEquivalentSpecifySLCBoardSpecifyPCLEquivalentSpecifyPCLBoardSpecifySLCSchoolDistrictIDSLCSchoolFullAddressPCLSchoolDistrictIDPCLSchoolFullAddressInstitutionTypeIDIdentificationTypeIDIdentificationNoFormSubmittedDateCheckedByIsAcceptedIsSubmittedPasswordRejectReasonExamSessionNameExamSessionDateADExamSessionDateBSCapacityExamDurationMinuteIsResultImmediatelyPageSizeShiftNameStartTimeEndTimeFormStatusHasStudentAttemptedExamFormIndexFacultyIDLevelIDShiftIDFiscalYearIDFacultyNameLevelNameFiscalYearNameRollNoStringPhotoDocumentIDExamRollNoActiveExamStartedTimeCreatedByCreatedDateModifiedByModifiedDateSLCBoardNamePCLBoardName
427472077-12702BindeshNaNNepalResunga municipality9.0Male2058/09/122001-12-27 00:00:00.000Shree resunga secondary school8460071 I2075BS3.6424602551Shree resunga secondary schoolNaNNaNBSNaNTekBahadurNepalLaxmiNaNNepal46.0GulmiNepaliLast Exam Admit CardBindesh NepalNaNNaN207720599.005BTZKU2021-205999.840957e+09bindeshnepal666@gmail.com1.01.0NaN1.057.09.044.03.01.01.01.01.00.0NaNNaNNaNNaNNaN44.0NaN44.0NaN0.04.0246025512021-01-17 00:00:00.000NaN1.01.0a3YNxNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012702.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283298.0NaN1.02021-02-10 13:00:00.0002.02021-01-17 17:24:09.1202.02021-01-17 17:25:26.780Nepal GovHSEB/NEB
427482077-12704RameshNaNKhadkaKamalbajar2.0Male2057/12/192001-04-01 00:00:00.000Rarahil memorial school0296031U2073BS3.7022506075DAV collegeNaNNaNBSNaNGaganSinghKhadkaSabitraNaNKhadka69.0AchhamNepaliCitizenshipRamesh Khadka86.6875.0207720600.01069792021-206009.863468e+09Rk1653886@gmail.com1.01.0NaN1.057.014.076.01.01.01.01.05.00.0NaNNaNNaNNaNNaN4.0NaN4.0NaN0.01.069-01-76-019422021-01-17 00:00:00.000NaN1.01.0Qt54WNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012704.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283302.0NaN1.02021-02-10 13:00:00.0000.02021-01-17 17:25:36.437NaNNaNNepal GovHSEB/NEB
427492077-12703SachinKumarGuptaBariyarpur2.0Male2050/07/191993-11-04 00:00:00.000Mitra High School0303847K2066BS82.6322503189Prasadi Academy62.202069.0BSNaNTarkeshworPrasadKalwarShilaDeviGupta32.0RautahatNepaliCitizenshipSachin Kumar GuptaNaNNaN207720601.0116914022021-206009.741243e+09hi.bksachin@gmail.com1.01.0NaN1.057.03.059.01.01.01.01.05.00.0NaNNaNNaNNaNNaN4.0NaN6.0NaN0.01.0323090/62021-01-17 00:00:00.000NaN1.01.0f8SGsNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012703.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283305.0NaN1.02021-02-10 13:00:00.0002.02021-01-17 17:25:36.877NaNNaNNepal GovHSEB/NEB
427502077-12705ManojNaNNath YogiSanpebagar4.0Male2058/09/252002-01-09 00:00:00.000Asian Public School0311593I2074BS3.90NP742/3618Global College International4.002020.0ADNaNBhajanNaNNathSaraswatiNaNNath69.0AchhamNepaliCitizenshipManoj Nath yogi59.43725.0207720602.005BU0WY2021-206029.741717e+09manojnath112@gmail.com1.01.0NaN1.057.014.076.01.01.05.05.05.00.0NaNNaNNaNNaNNaN4.0NaN4.0NaN0.01.069-01-75-000612021-01-17 00:00:00.000NaN1.01.0a5W6DNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012705.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283308.0NaN1.02021-02-10 13:00:00.0000.02021-01-17 17:55:57.397NaNNaNNepal GovCambridge University
427512077-12706ShrawanKumarDasIshorpur15.0Male2056/03/111999-06-25 00:00:00.000Shree Janta MA VI0222046 F2073BS2.4531701264Rajarshi Janak S.SNaNNaNBSNaNNawalKishorNaNDasParwatiDeviDas19.0SarlahiNepaliCitizenshipShrawan Kumar DasNaNNaN207720603.01053032021-206039.810016e+09shrawandas623@gmail.com1.01.0NaN1.057.02.030.01.01.01.01.01.00.0NaNNaNNaNNaNNaN30.0NaN32.0NaN0.01.019-01-74-023092021-01-17 00:00:00.000NaN1.01.0Yb89WNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012706.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283310.0NaN1.02021-02-10 13:00:00.0002.02021-01-17 17:58:31.427NaNNaNNepal GovHSEB/NEB
427522077-12708SunilNaNshresthaHimganga3.0Male2056/07/191999-11-05 00:00:00.000Renuka Devi Ma vi0183202F2071BS63.88204229Acme Engineering College71.532019.0ADNaNPanBahadurNewarTikaMayaNewar21.0RamechhapNepaliCitizenshipSunil ShresthaNaNNaN207720604.054194722021-206049.844316e+09xthasunil4@gmail.com1.01.0NaN1.057.02.028.01.01.03.04.05.00.0NaNNaNNaNNaNNaN28.0NaN28.0NaN0.01.021-01-72-036782021-01-17 00:00:00.000NaN1.01.0y7QHbNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012708.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN284687.0NaN1.02021-02-10 13:00:00.0002.02021-01-17 18:02:10.3009.02021-01-21 12:24:07.677Nepal GovCTEVT
427532077-12707AnkitNaNKhadkaHelambu3.0Male2057/04/082000-07-23 00:00:00.000Mahan Siddhartha School0316121M2073BS3.5522504653Advance Academy Secondary School2.652076.0BSNaNYubarajNaNKhadkaGomaNaNKhadka23.0SindhupalchokNepaliCitizenshipAnkit Khadka47.66382.0207720605.005BRB3G2021-206059.845436e+09ankitkhadka8848.86@gmail.com1.01.0NaN1.057.01.08.01.01.01.01.05.00.0NaNNaNNaNNaNNaN4.0NaN4.0NaN0.01.0241076002082021-01-17 00:00:00.000NaN1.01.0Et98PNaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012707.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283316.0NaN1.02021-02-10 13:00:00.0000.02021-01-17 18:12:46.447NaNNaNNepal GovHSEB/NEB
427542077-12709BuddhaNaNTamangChangunarayan2.0Male2058/02/102001-05-23 00:00:00.000Canvas boarding high school0306747Y2074BS3.7022600506Khwopa secodary schoolNaNNaNBSNaNChyangbaNaNTamangMailiNaNTamangni26.0BhaktapurNepaliCitizenshipBuddha TamangNaNNaN207720606.0116855362021-206069.803285e+09buddhatmg2468@gmail.com1.01.0NaN1.057.01.05.01.01.01.01.01.00.0NaNNaNNaNNaNNaN4.0NaN5.0NaN0.01.030-10-77-043842021-01-17 00:00:00.000NaN1.00.0Wr8s7NaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.00.012709.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283318.0NaN1.0NaN2.02021-01-17 18:19:56.153NaNNaNNepal GovHSEB/NEB
427552077-12710BibekNaNKhatriDhangadhi3.0Male2060/11/082004-02-20 00:00:00.000Bibek Khatri171008662074BS3.2017100866Aishwarya Videya NiketanNaNNaNBSNaNDineshNaNKhatriMamtaNaNKhatri71.0KailaliNepaliCitizenshipBibek KhatriNaNNaN207720607.005BR81S2021-206079.814600e+09Khatribipin19@gmail.com1.01.0NaN1.057.014.078.01.01.01.01.01.00.0NaNNaNNaNNaNNaN78.0NaN78.0NaN0.01.067-01-77-004442021-01-17 00:00:00.000NaN1.01.0Cy4n8NaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012710.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283321.0NaN1.02021-02-10 13:00:00.0000.02021-01-17 18:34:52.553NaNNaNNepal GovHSEB/NEB
427562077-12711AnujaNaNAdhikariChulachuli2.0Female2060/05/222003-09-08 00:00:00.000Gyanodaya Secondary School8290027E2074BS3.8427800649Gyanodaya Secondary SchoolNaNNaNBSNaNChandraNaNAdhikariShantaDeviKhatiwada3.0IlamNepaliCitizenshipAnuja AdhikariNaNNaN207720608.005BREUE2021-206089.863227e+09annuzaadhikari@gmail.com1.01.0NaN1.057.05.055.03.01.01.01.01.00.0NaNNaNNaNNaNNaN4.0NaN4.0NaN0.01.003-01-76-022542021-01-17 00:00:00.000NaN1.01.0Dg6k9NaN02/10/2021 Third(01:00 PM-03:00 PM)2021-02-10 00:00:00.0002077/10/28225.0120.00.020.0Third13:00:00.000000015:00:00.00000002.01.012711.02.01.03.019.0EngineeringBE / B.Arch.2077.0NaN283323.0NaN1.02021-02-10 13:00:00.0002.02021-01-17 19:13:29.910NaNNaNNepal GovHSEB/NEB